diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..c5d725a5e635d1d2e9f3690ac685cf26da8e2fac 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/last.ckpt.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/last.ckpt.meta filter=lfs diff=lfs merge=lfs -text
+Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/params.ckpt.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/params.ckpt.meta filter=lfs diff=lfs merge=lfs -text
diff --git a/Phoneme_Hallucinator_v2/.gitignore b/Phoneme_Hallucinator_v2/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..a19dfbcf9fdb61f534526a4e60debecea7074727
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/.gitignore
@@ -0,0 +1,6 @@
+.vscode
+exp/speech_XXL_cond
+*__pycache__
+*.pt
+*.npy
+*.wav
\ No newline at end of file
diff --git a/Phoneme_Hallucinator_v2/.vscode/launch.json b/Phoneme_Hallucinator_v2/.vscode/launch.json
new file mode 100644
index 0000000000000000000000000000000000000000..3f6e71212e170ac45b5c609f584fd28c524e37c6
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/.vscode/launch.json
@@ -0,0 +1,26 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Current File",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--cfg_file",
+                "exp/speech_XXL_cond/params.json",
+                "--num_samples",
+                "5000",
+                "--path",
+                "matching_set/target.pt",
+                "--out_path",
+                "matching_set/target_expanded_5k.npy"
+            ]
+        }
+    ]
+}
\ No newline at end of file
diff --git a/Phoneme_Hallucinator_v2/Phoneme Hallucinator DEMO.ipynb b/Phoneme_Hallucinator_v2/Phoneme Hallucinator DEMO.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..aa65ea1b41400e2b61d8f72d79ec243147d8a783
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/Phoneme Hallucinator DEMO.ipynb	
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","source":["Because our Hallucinator is implemented by Tensorflow 1.15, we need to install python 3.6 first. GPU is not required in this notebook but we recommend to have one."],"metadata":{"id":"M1i-crl3Pr3o"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"NZuh4QxCyBVi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1691780902209,"user_tz":420,"elapsed":17166,"user":{"displayName":"Siyuan Shan","userId":"16492559305772335155"}},"outputId":"21ac26bb-4839-4915-cbe9-e5313135e091"},"outputs":[{"output_type":"stream","name":"stdout","text":["PREFIX=/usr/local\n","installing: python-3.6.5-hc3d631a_2 ...\n","installing: ca-certificates-2018.03.07-0 ...\n","installing: conda-env-2.6.0-h36134e3_1 ...\n","installing: libgcc-ng-7.2.0-hdf63c60_3 ...\n","installing: libstdcxx-ng-7.2.0-hdf63c60_3 ...\n","installing: libffi-3.2.1-hd88cf55_4 ...\n","installing: ncurses-6.1-hf484d3e_0 ...\n","installing: openssl-1.0.2o-h20670df_0 ...\n","installing: tk-8.6.7-hc745277_3 ...\n","installing: xz-5.2.4-h14c3975_4 ...\n","installing: yaml-0.1.7-had09818_2 ...\n","installing: zlib-1.2.11-ha838bed_2 ...\n","installing: libedit-3.1.20170329-h6b74fdf_2 ...\n","installing: readline-7.0-ha6073c6_4 ...\n","installing: sqlite-3.23.1-he433501_0 ...\n","installing: asn1crypto-0.24.0-py36_0 ...\n","installing: certifi-2018.4.16-py36_0 ...\n","installing: chardet-3.0.4-py36h0f667ec_1 ...\n","installing: idna-2.6-py36h82fb2a8_1 ...\n","installing: pycosat-0.6.3-py36h0a5515d_0 ...\n","installing: pycparser-2.18-py36hf9f622e_1 ...\n","installing: pysocks-1.6.8-py36_0 ...\n","installing: ruamel_yaml-0.15.37-py36h14c3975_2 ...\n","installing: six-1.11.0-py36h372c433_1 ...\n","installing: cffi-1.11.5-py36h9745a5d_0 ...\n","installing: setuptools-39.2.0-py36_0 ...\n","installing: cryptography-2.2.2-py36h14c3975_0 ...\n","installing: wheel-0.31.1-py36_0 ...\n","installing: pip-10.0.1-py36_0 ...\n","installing: pyopenssl-18.0.0-py36_0 ...\n","installing: urllib3-1.22-py36hbe7ace6_0 ...\n","installing: requests-2.18.4-py36he2e5f8d_1 ...\n","installing: conda-4.5.4-py36_0 ...\n","installation finished.\n","WARNING:\n","    You currently have a PYTHONPATH environment variable set. This may cause\n","    unexpected behavior when running the Python interpreter in Miniconda3.\n","    For best results, please verify that your PYTHONPATH only points to\n","    directories of packages that are compatible with the Python interpreter\n","    in Miniconda3: /usr/local\n"]},{"output_type":"stream","name":"stderr","text":["--2023-08-11 19:08:04--  https://repo.continuum.io/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh\n","Resolving repo.continuum.io (repo.continuum.io)... 104.18.201.79, 104.18.200.79, 2606:4700::6812:c94f, ...\n","Connecting to repo.continuum.io (repo.continuum.io)|104.18.201.79|:443... connected.\n","HTTP request sent, awaiting response... 301 Moved Permanently\n","Location: https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh [following]\n","--2023-08-11 19:08:04--  https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh\n","Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8203, 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9200K .......... .......... .......... .......... .......... 16%  240M 1s\n","  9250K .......... .......... .......... .......... .......... 16%  109M 1s\n","  9300K .......... .......... .......... .......... .......... 16% 64.4M 1s\n","  9350K .......... .......... .......... .......... .......... 16% 76.1M 1s\n","  9400K .......... .......... .......... .......... .......... 16%  266M 1s\n","  9450K .......... .......... .......... .......... .......... 16%  306M 1s\n","  9500K .......... .......... .......... .......... .......... 16%  281M 1s\n","  9550K .......... .......... .......... .......... .......... 16% 24.3M 1s\n","  9600K .......... .......... .......... .......... .......... 16%  160M 1s\n","  9650K .......... .......... .......... .......... .......... 16%  206M 1s\n","  9700K .......... .......... .......... .......... .......... 17%  243M 1s\n","  9750K .......... .......... .......... .......... .......... 17%  226M 1s\n","  9800K .......... .......... .......... .......... .......... 17%  237M 1s\n","  9850K .......... .......... .......... .......... .......... 17%  226M 1s\n","  9900K .......... .......... .......... .......... .......... 17%  239M 1s\n","  9950K .......... .......... .......... .......... .......... 17%  216M 1s\n"," 10000K .......... .......... .......... .......... .......... 17%  262M 1s\n"," 10050K .......... .......... .......... .......... .......... 17%  227M 1s\n"," 10100K .......... .......... .......... .......... .......... 17%  258M 1s\n"," 10150K .......... .......... .......... .......... .......... 17%  212M 1s\n"," 10200K .......... .......... .......... .......... .......... 17%  253M 1s\n"," 10250K .......... .......... .......... .......... .......... 18%  257M 1s\n"," 10300K .......... .......... .......... .......... .......... 18%  241M 1s\n"," 10350K .......... .......... .......... .......... .......... 18%  151M 1s\n"," 10400K .......... .......... .......... .......... .......... 18%  236M 1s\n"," 10450K .......... .......... .......... .......... .......... 18%  256M 1s\n"," 10500K .......... .......... .......... .......... .......... 18%  180M 1s\n"," 10550K .......... .......... .......... .......... .......... 18%  219M 1s\n"," 10600K .......... .......... .......... .......... .......... 18%  234M 1s\n"," 10650K .......... .......... .......... .......... .......... 18%  242M 1s\n"," 10700K .......... .......... .......... .......... .......... 18%  255M 1s\n"," 10750K .......... .......... .......... .......... .......... 18%  197M 1s\n"," 10800K .......... .......... .......... .......... .......... 19%  252M 1s\n"," 10850K .......... .......... .......... .......... .......... 19%  226M 1s\n"," 10900K .......... .......... .......... .......... .......... 19%  249M 1s\n"," 10950K .......... .......... .......... .......... .......... 19%  218M 1s\n"," 11000K .......... .......... .......... .......... .......... 19%  248M 1s\n"," 11050K .......... .......... .......... .......... .......... 19%  255M 1s\n"," 11100K .......... .......... .......... .......... .......... 19%  239M 1s\n"," 11150K .......... .......... .......... .......... .......... 19%  205M 1s\n"," 11200K .......... .......... .......... .......... .......... 19%  245M 1s\n"," 11250K .......... .......... .......... .......... .......... 19%  247M 1s\n"," 11300K .......... .......... .......... .......... .......... 19%  246M 1s\n"," 11350K .......... .......... .......... .......... .......... 19%  220M 1s\n"," 11400K .......... .......... .......... .......... .......... 20%  161M 1s\n"," 11450K .......... .......... .......... .......... .......... 20%  292M 1s\n"," 11500K .......... .......... .......... .......... .......... 20%  301M 1s\n"," 11550K .......... .......... .......... .......... .......... 20%  117M 1s\n"," 11600K .......... .......... .......... .......... .......... 20%  246M 1s\n"," 11650K .......... .......... .......... .......... .......... 20%  231M 1s\n"," 11700K .......... .......... .......... .......... .......... 20%  256M 1s\n"," 11750K .......... .......... .......... .......... .......... 20%  213M 1s\n"," 11800K .......... .......... .......... .......... .......... 20%  196M 1s\n"," 11850K .......... .......... .......... .......... .......... 20%  222M 1s\n"," 11900K .......... .......... .......... .......... .......... 20%  230M 1s\n"," 11950K .......... .......... .......... .......... .......... 21%  184M 1s\n"," 12000K .......... .......... .......... .......... .......... 21%  153M 1s\n"," 12050K .......... .......... .......... .......... .......... 21%  238M 1s\n"," 12100K .......... .......... .......... .......... .......... 21%  254M 1s\n"," 12150K .......... .......... .......... .......... .......... 21%  211M 1s\n"," 12200K .......... .......... .......... .......... .......... 21%  245M 1s\n"," 12250K .......... .......... .......... .......... .......... 21%  250M 1s\n"," 12300K .......... .......... .......... .......... .......... 21%  241M 1s\n"," 12350K .......... .......... .......... .......... .......... 21%  212M 1s\n"," 12400K .......... .......... .......... .......... .......... 21%  221M 1s\n"," 12450K .......... .......... .......... .......... .......... 21%  243M 1s\n"," 12500K .......... .......... .......... .......... .......... 21%  267M 1s\n"," 12550K .......... .......... .......... .......... .......... 22%  225M 1s\n"," 12600K .......... .......... .......... .......... .......... 22%  238M 1s\n"," 12650K .......... .......... .......... .......... .......... 22%  236M 1s\n"," 12700K .......... .......... .......... .......... .......... 22%  201M 1s\n"," 12750K .......... .......... .......... .......... .......... 22%  136M 1s\n"," 12800K .......... .......... .......... .......... .......... 22%  246M 1s\n"," 12850K .......... .......... .......... .......... .......... 22%  218M 1s\n"," 12900K .......... .......... .......... .......... .......... 22%  239M 1s\n"," 12950K .......... .......... .......... .......... .......... 22%  222M 1s\n"," 13000K .......... .......... .......... .......... .......... 22%  251M 1s\n"," 13050K .......... .......... .......... .......... .......... 22%  259M 1s\n"," 13100K .......... .......... .......... .......... .......... 23%  240M 1s\n"," 13150K .......... .......... .......... .......... .......... 23%  200M 1s\n"," 13200K .......... .......... .......... .......... .......... 23%  260M 1s\n"," 13250K .......... .......... .......... .......... .......... 23%  249M 1s\n"," 13300K .......... .......... .......... .......... .......... 23%  255M 1s\n"," 13350K .......... .......... .......... .......... .......... 23%  215M 1s\n"," 13400K .......... .......... .......... .......... .......... 23%  243M 1s\n"," 13450K .......... .......... .......... .......... .......... 23%  231M 1s\n"," 13500K .......... .......... .......... .......... .......... 23%  354M 1s\n"," 13550K .......... .......... .......... .......... .......... 23%  367M 1s\n"," 13600K .......... .......... .......... .......... .......... 23%  426M 1s\n"," 13650K .......... .......... .......... .......... .......... 23%  151M 1s\n"," 13700K .......... .......... .......... .......... .......... 24%  151M 1s\n"," 13750K .......... .......... .......... .......... .......... 24%  206M 1s\n"," 13800K .......... .......... .......... .......... .......... 24%  135M 1s\n"," 13850K .......... .......... .......... .......... .......... 24%  147M 1s\n"," 13900K .......... .......... .......... .......... .......... 24%  124M 1s\n"," 13950K .......... .......... .......... .......... .......... 24%  150M 1s\n"," 14000K .......... .......... .......... .......... .......... 24%  243M 1s\n"," 14050K .......... .......... .......... .......... .......... 24%  243M 1s\n"," 14100K .......... .......... .......... .......... .......... 24%  191M 1s\n"," 14150K .......... .......... .......... .......... .......... 24%  179M 1s\n"," 14200K .......... .......... .......... .......... .......... 24%  242M 1s\n"," 14250K .......... .......... .......... .......... .......... 25%  251M 1s\n"," 14300K .......... .......... .......... .......... .......... 25%  170M 1s\n"," 14350K .......... .......... .......... .......... .......... 25%  137M 1s\n"," 14400K .......... .......... .......... .......... .......... 25%  264M 1s\n"," 14450K .......... .......... .......... .......... .......... 25%  244M 1s\n"," 14500K .......... .......... .......... .......... .......... 25%  266M 1s\n"," 14550K .......... .......... .......... .......... .......... 25%  202M 1s\n"," 14600K .......... .......... .......... .......... .......... 25%  250M 1s\n"," 14650K .......... .......... .......... .......... .......... 25%  256M 1s\n"," 14700K .......... .......... .......... .......... .......... 25%  242M 1s\n"," 14750K .......... .......... .......... .......... .......... 25%  211M 1s\n"," 14800K .......... .......... .......... .......... .......... 26%  241M 1s\n"," 14850K .......... .......... .......... .......... .......... 26%  255M 1s\n"," 14900K .......... .......... .......... .......... .......... 26%  259M 1s\n"," 14950K .......... .......... .......... .......... .......... 26%  213M 1s\n"," 15000K .......... .......... .......... .......... .......... 26%  161M 1s\n"," 15050K .......... .......... .......... .......... .......... 26%  192M 1s\n"," 15100K .......... .......... .......... .......... .......... 26%  189M 1s\n"," 15150K .......... .......... .......... .......... .......... 26%  146M 1s\n"," 15200K .......... .......... .......... .......... .......... 26%  171M 1s\n"," 15250K .......... .......... .......... .......... .......... 26%  181M 1s\n"," 15300K .......... .......... .......... .......... .......... 26%  191M 1s\n"," 15350K .......... .......... .......... .......... .......... 26%  235M 1s\n"," 15400K .......... .......... .......... .......... .......... 27%  245M 1s\n"," 15450K .......... .......... .......... .......... .......... 27%  241M 1s\n"," 15500K .......... .......... .......... .......... .......... 27%  264M 1s\n"," 15550K .......... .......... .......... .......... .......... 27%  193M 1s\n"," 15600K .......... .......... .......... .......... .......... 27%  260M 1s\n"," 15650K .......... .......... .......... .......... .......... 27%  234M 1s\n"," 15700K .......... .......... .......... .......... .......... 27%  246M 0s\n"," 15750K .......... .......... .......... .......... .......... 27%  228M 0s\n"," 15800K .......... .......... .......... .......... .......... 27%  173M 0s\n"," 15850K .......... .......... .......... .......... .......... 27%  198M 0s\n"," 15900K .......... .......... .......... .......... .......... 27%  197M 0s\n"," 15950K .......... .......... .......... .......... .......... 28%  159M 0s\n"," 16000K .......... .......... .......... .......... .......... 28%  202M 0s\n"," 16050K .......... .......... .......... .......... .......... 28%  205M 0s\n"," 16100K .......... .......... .......... .......... .......... 28%  168M 0s\n"," 16150K .......... .......... .......... .......... .......... 28%  166M 0s\n"," 16200K .......... .......... .......... .......... .......... 28%  162M 0s\n"," 16250K .......... .......... .......... .......... .......... 28%  178M 0s\n"," 16300K .......... .......... .......... .......... .......... 28%  208M 0s\n"," 16350K .......... .......... .......... .......... .......... 28%  169M 0s\n"," 16400K .......... .......... .......... .......... .......... 28%  206M 0s\n"," 16450K .......... .......... .......... .......... .......... 28%  194M 0s\n"," 16500K .......... .......... .......... .......... .......... 28%  184M 0s\n"," 16550K .......... .......... .......... .......... .......... 29%  158M 0s\n"," 16600K .......... .......... .......... .......... .......... 29%  193M 0s\n"," 16650K .......... .......... .......... .......... .......... 29%  188M 0s\n"," 16700K .......... .......... .......... .......... .......... 29%  203M 0s\n"," 16750K .......... .......... .......... .......... .......... 29%  200M 0s\n"," 16800K .......... .......... .......... .......... .......... 29%  258M 0s\n"," 16850K .......... .......... .......... .......... .......... 29%  239M 0s\n"," 16900K .......... .......... .......... .......... .......... 29%  252M 0s\n"," 16950K .......... .......... .......... .......... .......... 29%  219M 0s\n"," 17000K .......... .......... .......... .......... .......... 29%  240M 0s\n"," 17050K .......... .......... .......... .......... .......... 29%  191M 0s\n"," 17100K .......... .......... .......... .......... .......... 30%  200M 0s\n"," 17150K .......... .......... .......... .......... .......... 30%  163M 0s\n"," 17200K .......... .......... .......... .......... .......... 30%  174M 0s\n"," 17250K .......... .......... .......... .......... .......... 30%  167M 0s\n"," 17300K .......... .......... .......... .......... .......... 30%  219M 0s\n"," 17350K .......... .......... .......... .......... .......... 30%  144M 0s\n"," 17400K .......... .......... .......... .......... .......... 30%  233M 0s\n"," 17450K .......... .......... .......... .......... .......... 30%  185M 0s\n"," 17500K .......... .......... .......... .......... .......... 30%  165M 0s\n"," 17550K .......... .......... .......... .......... .......... 30%  140M 0s\n"," 17600K .......... .......... .......... .......... .......... 30%  230M 0s\n"," 17650K .......... .......... .......... .......... .......... 30%  184M 0s\n"," 17700K .......... .......... .......... .......... .......... 31%  200M 0s\n"," 17750K .......... .......... .......... .......... .......... 31%  151M 0s\n"," 17800K .......... .......... .......... .......... .......... 31%  205M 0s\n"," 17850K .......... .......... .......... .......... .......... 31%  184M 0s\n"," 17900K .......... .......... .......... .......... .......... 31%  179M 0s\n"," 17950K .......... .......... .......... .......... .......... 31%  156M 0s\n"," 18000K .......... .......... .......... .......... .......... 31%  177M 0s\n"," 18050K .......... .......... .......... .......... .......... 31%  208M 0s\n"," 18100K .......... .......... .......... .......... .......... 31%  215M 0s\n"," 18150K .......... .......... .......... .......... .......... 31%  149M 0s\n"," 18200K .......... .......... .......... .......... .......... 31%  195M 0s\n"," 18250K .......... .......... .......... .......... .......... 32%  156M 0s\n"," 18300K .......... .......... .......... .......... .......... 32%  196M 0s\n"," 18350K .......... .......... .......... .......... .......... 32%  154M 0s\n"," 18400K .......... .......... .......... .......... .......... 32%  197M 0s\n"," 18450K .......... .......... .......... .......... .......... 32%  169M 0s\n"," 18500K .......... .......... .......... .......... .......... 32%  196M 0s\n"," 18550K .......... .......... .......... .......... .......... 32%  161M 0s\n"," 18600K .......... .......... .......... .......... .......... 32%  211M 0s\n"," 18650K .......... .......... .......... .......... .......... 32%  196M 0s\n"," 18700K .......... .......... .......... .......... .......... 32%  165M 0s\n"," 18750K .......... .......... .......... .......... .......... 32%  145M 0s\n"," 18800K .......... .......... .......... .......... .......... 33%  192M 0s\n"," 18850K .......... .......... .......... .......... .......... 33%  202M 0s\n"," 18900K .......... .......... .......... .......... .......... 33%  171M 0s\n"," 18950K .......... .......... .......... .......... .......... 33%  169M 0s\n"," 19000K .......... .......... .......... .......... .......... 33%  183M 0s\n"," 19050K .......... .......... .......... .......... .......... 33%  200M 0s\n"," 19100K .......... .......... .......... .......... .......... 33%  219M 0s\n"," 19150K .......... .......... .......... .......... .......... 33%  161M 0s\n"," 19200K .......... .......... .......... .......... .......... 33%  204M 0s\n"," 19250K .......... .......... .......... .......... .......... 33%  192M 0s\n"," 19300K .......... .......... .......... .......... .......... 33%  122M 0s\n"," 19350K .......... .......... .......... .......... .......... 33%  137M 0s\n"," 19400K .......... .......... .......... .......... .......... 34%  165M 0s\n"," 19450K .......... .......... .......... .......... .......... 34%  166M 0s\n"," 19500K .......... .......... .......... .......... .......... 34%  166M 0s\n"," 19550K .......... .......... .......... .......... .......... 34% 95.5M 0s\n"," 19600K .......... .......... .......... .......... .......... 34%  156M 0s\n"," 19650K .......... .......... .......... .......... .......... 34%  172M 0s\n"," 19700K .......... .......... .......... .......... .......... 34%  217M 0s\n"," 19750K .......... .......... .......... .......... .......... 34%  132M 0s\n"," 19800K .......... .......... .......... .......... .......... 34%  216M 0s\n"," 19850K .......... .......... .......... .......... .......... 34%  208M 0s\n"," 19900K .......... .......... .......... .......... .......... 34%  207M 0s\n"," 19950K .......... .......... .......... .......... .......... 35%  130M 0s\n"," 20000K .......... .......... .......... .......... .......... 35%  196M 0s\n"," 20050K .......... .......... .......... .......... .......... 35%  178M 0s\n"," 20100K .......... .......... .......... .......... .......... 35%  168M 0s\n"," 20150K .......... .......... .......... .......... .......... 35%  193M 0s\n"," 20200K .......... .......... .......... .......... .......... 35%  164M 0s\n"," 20250K .......... .......... .......... .......... .......... 35%  177M 0s\n"," 20300K .......... .......... .......... .......... .......... 35%  200M 0s\n"," 20350K .......... .......... .......... .......... .......... 35%  155M 0s\n"," 20400K .......... .......... .......... .......... .......... 35%  172M 0s\n"," 20450K .......... .......... .......... .......... .......... 35%  177M 0s\n"," 20500K .......... .......... .......... .......... .......... 35%  196M 0s\n"," 20550K .......... .......... .......... .......... .......... 36%  165M 0s\n"," 20600K .......... .......... .......... .......... .......... 36%  206M 0s\n"," 20650K .......... .......... .......... .......... .......... 36%  191M 0s\n"," 20700K .......... .......... .......... .......... .......... 36%  171M 0s\n"," 20750K .......... .......... .......... .......... .......... 36%  173M 0s\n"," 20800K .......... .......... .......... .......... .......... 36%  226M 0s\n"," 20850K .......... .......... .......... .......... .......... 36%  164M 0s\n"," 20900K .......... .......... .......... .......... .......... 36%  225M 0s\n"," 20950K .......... .......... .......... .......... .......... 36%  191M 0s\n"," 21000K .......... .......... .......... .......... .......... 36%  172M 0s\n"," 21050K .......... .......... .......... .......... .......... 36%  204M 0s\n"," 21100K .......... .......... .......... .......... .......... 37%  247M 0s\n"," 21150K .......... .......... .......... .......... .......... 37%  174M 0s\n"," 21200K .......... .......... .......... .......... .......... 37%  184M 0s\n"," 21250K .......... .......... .......... .......... .......... 37%  152M 0s\n"," 21300K .......... .......... .......... .......... .......... 37%  207M 0s\n"," 21350K .......... .......... .......... .......... .......... 37%  199M 0s\n"," 21400K .......... .......... .......... .......... .......... 37%  155M 0s\n"," 21450K .......... .......... .......... .......... .......... 37%  202M 0s\n"," 21500K .......... .......... .......... .......... .......... 37%  222M 0s\n"," 21550K .......... .......... .......... .......... .......... 37%  184M 0s\n"," 21600K .......... .......... .......... .......... .......... 37%  209M 0s\n"," 21650K .......... .......... .......... .......... .......... 38%  200M 0s\n"," 21700K .......... .......... .......... .......... .......... 38%  168M 0s\n"," 21750K .......... .......... .......... .......... .......... 38%  184M 0s\n"," 21800K .......... .......... .......... .......... .......... 38%  171M 0s\n"," 21850K .......... .......... .......... .......... .......... 38%  184M 0s\n"," 21900K .......... .......... .......... .......... .......... 38%  256M 0s\n"," 21950K .......... .......... .......... .......... .......... 38%  181M 0s\n"," 22000K .......... .......... .......... .......... .......... 38%  169M 0s\n"," 22050K .......... .......... .......... .......... .......... 38%  194M 0s\n"," 22100K .......... .......... .......... .......... .......... 38%  247M 0s\n"," 22150K .......... .......... .......... .......... .......... 38%  189M 0s\n"," 22200K .......... .......... .......... .......... .......... 38%  163M 0s\n"," 22250K .......... .......... .......... .......... .......... 39%  178M 0s\n"," 22300K .......... .......... .......... .......... .......... 39%  201M 0s\n"," 22350K .......... .......... .......... .......... .......... 39% 24.8M 0s\n"," 22400K .......... .......... .......... .......... .......... 39%  138M 0s\n"," 22450K .......... .......... .......... .......... .......... 39%  186M 0s\n"," 22500K .......... .......... .......... .......... .......... 39%  172M 0s\n"," 22550K .......... .......... .......... .......... .......... 39%  184M 0s\n"," 22600K .......... .......... .......... .......... .......... 39%  196M 0s\n"," 22650K .......... .......... .......... .......... .......... 39%  167M 0s\n"," 22700K .......... .......... .......... .......... .......... 39%  213M 0s\n"," 22750K .......... .......... .......... .......... .......... 39%  203M 0s\n"," 22800K .......... .......... .......... .......... .......... 40%  228M 0s\n"," 22850K .......... .......... .......... .......... .......... 40%  157M 0s\n"," 22900K .......... .......... .......... .......... .......... 40%  217M 0s\n"," 22950K .......... .......... .......... .......... .......... 40%  168M 0s\n"," 23000K .......... .......... .......... .......... .......... 40%  153M 0s\n"," 23050K .......... .......... .......... .......... .......... 40%  195M 0s\n"," 23100K .......... .......... .......... .......... .......... 40%  192M 0s\n"," 23150K .......... .......... .......... .......... .......... 40%  132M 0s\n"," 23200K .......... .......... .......... .......... .......... 40%  195M 0s\n"," 23250K .......... .......... .......... .......... .......... 40%  220M 0s\n"," 23300K .......... .......... .......... .......... .......... 40%  247M 0s\n"," 23350K .......... .......... .......... .......... .......... 40%  134M 0s\n"," 23400K .......... .......... .......... .......... .......... 41%  183M 0s\n"," 23450K .......... .......... .......... .......... .......... 41%  235M 0s\n"," 23500K .......... .......... .......... .......... .......... 41%  191M 0s\n"," 23550K .......... .......... .......... .......... .......... 41%  156M 0s\n"," 23600K .......... .......... .......... .......... .......... 41%  183M 0s\n"," 23650K .......... .......... .......... .......... .......... 41%  189M 0s\n"," 23700K .......... .......... .......... .......... .......... 41%  197M 0s\n"," 23750K .......... .......... .......... .......... .......... 41%  187M 0s\n"," 23800K .......... .......... .......... .......... .......... 41%  194M 0s\n"," 23850K .......... .......... .......... .......... .......... 41%  231M 0s\n"," 23900K .......... .......... .......... .......... .......... 41%  207M 0s\n"," 23950K .......... .......... .......... .......... .......... 42%  145M 0s\n"," 24000K .......... .......... .......... .......... .......... 42%  188M 0s\n"," 24050K .......... .......... .......... .......... .......... 42%  199M 0s\n"," 24100K .......... .......... .......... .......... .......... 42%  197M 0s\n"," 24150K .......... .......... .......... .......... .......... 42%  206M 0s\n"," 24200K .......... .......... .......... .......... .......... 42%  227M 0s\n"," 24250K .......... .......... .......... .......... .......... 42%  251M 0s\n"," 24300K .......... .......... .......... .......... .......... 42%  250M 0s\n"," 24350K .......... .......... .......... .......... .......... 42%  198M 0s\n"," 24400K .......... .......... .......... .......... .......... 42%  164M 0s\n"," 24450K .......... .......... .......... .......... .......... 42%  200M 0s\n"," 24500K .......... .......... .......... .......... .......... 42%  169M 0s\n"," 24550K .......... .......... .......... .......... .......... 43%  203M 0s\n"," 24600K .......... .......... .......... .......... .......... 43%  213M 0s\n"," 24650K .......... .......... .......... .......... .......... 43%  155M 0s\n"," 24700K .......... .......... .......... .......... .......... 43%  161M 0s\n"," 24750K .......... .......... .......... .......... .......... 43%  164M 0s\n"," 24800K .......... .......... .......... .......... .......... 43%  254M 0s\n"," 24850K .......... .......... .......... .......... .......... 43%  206M 0s\n"," 24900K .......... .......... .......... .......... .......... 43%  137M 0s\n"," 24950K .......... .......... .......... .......... .......... 43%  201M 0s\n"," 25000K .......... .......... .......... .......... .......... 43%  240M 0s\n"," 25050K .......... .......... .......... .......... .......... 43%  170M 0s\n"," 25100K .......... .......... .......... .......... .......... 44%  155M 0s\n"," 25150K .......... .......... .......... .......... .......... 44%  156M 0s\n"," 25200K .......... .......... .......... .......... .......... 44%  236M 0s\n"," 25250K .......... .......... .......... .......... .......... 44%  244M 0s\n"," 25300K .......... .......... .......... .......... .......... 44%  228M 0s\n"," 25350K .......... .......... .......... .......... .......... 44%  225M 0s\n"," 25400K .......... .......... .......... .......... .......... 44%  162M 0s\n"," 25450K .......... .......... .......... .......... .......... 44%  187M 0s\n"," 25500K .......... .......... .......... .......... .......... 44%  188M 0s\n"," 25550K .......... .......... .......... .......... .......... 44%  171M 0s\n"," 25600K .......... .......... .......... .......... .......... 44%  193M 0s\n"," 25650K .......... .......... .......... .......... .......... 45%  155M 0s\n"," 25700K .......... .......... .......... .......... .......... 45%  173M 0s\n"," 25750K .......... .......... .......... .......... .......... 45%  185M 0s\n"," 25800K .......... .......... .......... .......... .......... 45%  249M 0s\n"," 25850K .......... .......... .......... .......... .......... 45%  251M 0s\n"," 25900K .......... .......... .......... .......... .......... 45%  132M 0s\n"," 25950K .......... .......... .......... .......... .......... 45%  175M 0s\n"," 26000K .......... .......... .......... .......... .......... 45%  252M 0s\n"," 26050K .......... .......... .......... .......... .......... 45%  172M 0s\n"," 26100K .......... .......... .......... .......... .......... 45%  167M 0s\n"," 26150K .......... .......... .......... .......... .......... 45%  191M 0s\n"," 26200K .......... .......... .......... .......... .......... 45%  241M 0s\n"," 26250K .......... .......... .......... .......... .......... 46%  235M 0s\n"," 26300K .......... .......... .......... .......... .......... 46%  242M 0s\n"," 26350K .......... .......... .......... .......... .......... 46%  207M 0s\n"," 26400K .......... .......... .......... .......... .......... 46%  213M 0s\n"," 26450K .......... .......... .......... .......... .......... 46%  180M 0s\n"," 26500K .......... .......... .......... .......... .......... 46%  231M 0s\n"," 26550K .......... .......... .......... .......... .......... 46%  157M 0s\n"," 26600K .......... .......... .......... .......... .......... 46%  188M 0s\n"," 26650K .......... .......... .......... .......... .......... 46%  262M 0s\n"," 26700K .......... .......... .......... .......... .......... 46%  164M 0s\n"," 26750K .......... .......... .......... .......... .......... 46%  148M 0s\n"," 26800K .......... .......... .......... .......... .......... 47%  191M 0s\n"," 26850K .......... .......... .......... .......... .......... 47%  223M 0s\n"," 26900K .......... .......... .......... .......... .......... 47%  258M 0s\n"," 26950K .......... .......... .......... .......... .......... 47%  158M 0s\n"," 27000K .......... .......... .......... .......... .......... 47%  175M 0s\n"," 27050K .......... .......... .......... .......... .......... 47%  207M 0s\n"," 27100K .......... .......... .......... .......... .......... 47%  249M 0s\n"," 27150K .......... .......... .......... .......... .......... 47%  164M 0s\n"," 27200K .......... .......... .......... .......... .......... 47%  176M 0s\n"," 27250K .......... .......... .......... .......... .......... 47%  166M 0s\n"," 27300K .......... .......... .......... .......... .......... 47%  230M 0s\n"," 27350K .......... .......... .......... .......... .......... 47%  233M 0s\n"," 27400K .......... .......... .......... .......... .......... 48%  263M 0s\n"," 27450K .......... .......... .......... .......... .......... 48%  217M 0s\n"," 27500K .......... .......... .......... .......... .......... 48%  164M 0s\n"," 27550K .......... .......... .......... .......... .......... 48%  161M 0s\n"," 27600K .......... .......... .......... .......... .......... 48%  193M 0s\n"," 27650K .......... .......... .......... .......... .......... 48%  249M 0s\n"," 27700K .......... .......... .......... .......... .......... 48%  158M 0s\n"," 27750K .......... .......... .......... .......... .......... 48%  149M 0s\n"," 27800K .......... .......... .......... .......... .......... 48%  235M 0s\n"," 27850K .......... .......... .......... .......... .......... 48%  242M 0s\n"," 27900K .......... .......... .......... .......... .......... 48%  260M 0s\n"," 27950K .......... .......... .......... .......... .......... 49%  191M 0s\n"," 28000K .......... .......... .......... .......... .......... 49%  186M 0s\n"," 28050K .......... .......... .......... .......... .......... 49%  170M 0s\n"," 28100K .......... .......... .......... .......... .......... 49%  187M 0s\n"," 28150K .......... .......... .......... .......... .......... 49%  218M 0s\n"," 28200K .......... .......... .......... .......... .......... 49%  163M 0s\n"," 28250K .......... .......... .......... .......... .......... 49%  173M 0s\n"," 28300K .......... .......... .......... .......... .......... 49%  177M 0s\n"," 28350K .......... .......... .......... .......... .......... 49%  215M 0s\n"," 28400K .......... .......... .......... .......... .......... 49%  224M 0s\n"," 28450K .......... .......... .......... .......... .......... 49%  259M 0s\n"," 28500K .......... .......... .......... .......... .......... 50%  188M 0s\n"," 28550K .......... .......... .......... .......... .......... 50%  162M 0s\n"," 28600K .......... .......... .......... .......... .......... 50%  222M 0s\n"," 28650K .......... .......... .......... .......... .......... 50%  166M 0s\n"," 28700K .......... .......... .......... .......... .......... 50%  218M 0s\n"," 28750K .......... .......... .......... .......... .......... 50%  201M 0s\n"," 28800K .......... .......... .......... .......... .......... 50%  255M 0s\n"," 28850K .......... .......... .......... .......... .......... 50%  214M 0s\n"," 28900K .......... .......... .......... .......... .......... 50%  214M 0s\n"," 28950K .......... .......... .......... .......... .......... 50%  230M 0s\n"," 29000K .......... .......... .......... .......... .......... 50%  255M 0s\n"," 29050K .......... .......... .......... .......... .......... 50%  245M 0s\n"," 29100K .......... .......... .......... .......... .......... 51%  249M 0s\n"," 29150K .......... .......... .......... .......... .......... 51%  115M 0s\n"," 29200K .......... .......... .......... .......... .......... 51%  181M 0s\n"," 29250K .......... .......... .......... .......... .......... 51%  220M 0s\n"," 29300K .......... .......... .......... .......... .......... 51%  159M 0s\n"," 29350K .......... .......... .......... .......... .......... 51%  181M 0s\n"," 29400K .......... .......... .......... .......... .......... 51%  212M 0s\n"," 29450K .......... .......... .......... .......... .......... 51%  185M 0s\n"," 29500K .......... .......... .......... .......... .......... 51%  188M 0s\n"," 29550K .......... .......... .......... .......... .......... 51%  170M 0s\n"," 29600K .......... .......... .......... .......... .......... 51%  211M 0s\n"," 29650K .......... .......... .......... .......... .......... 52%  180M 0s\n"," 29700K .......... .......... .......... .......... .......... 52%  197M 0s\n"," 29750K .......... .......... .......... .......... .......... 52%  228M 0s\n"," 29800K .......... .......... .......... .......... .......... 52%  153M 0s\n"," 29850K .......... .......... .......... .......... .......... 52%  238M 0s\n"," 29900K .......... .......... .......... .......... .......... 52%  141M 0s\n"," 29950K .......... .......... .......... .......... .......... 52%  210M 0s\n"," 30000K .......... .......... .......... .......... .......... 52%  237M 0s\n"," 30050K .......... .......... .......... .......... .......... 52%  176M 0s\n"," 30100K .......... .......... .......... .......... .......... 52%  144M 0s\n"," 30150K .......... .......... .......... .......... .......... 52%  169M 0s\n"," 30200K .......... .......... .......... .......... .......... 52%  235M 0s\n"," 30250K .......... .......... .......... .......... .......... 53%  228M 0s\n"," 30300K .......... .......... .......... .......... .......... 53%  147M 0s\n"," 30350K .......... .......... .......... .......... .......... 53%  137M 0s\n"," 30400K .......... .......... .......... .......... .......... 53%  206M 0s\n"," 30450K .......... .......... .......... .......... .......... 53%  245M 0s\n"," 30500K .......... .......... .......... .......... .......... 53% 93.8M 0s\n"," 30550K .......... .......... .......... .......... .......... 53% 81.3M 0s\n"," 30600K .......... .......... .......... .......... .......... 53%  239M 0s\n"," 30650K .......... .......... .......... .......... .......... 53% 54.7M 0s\n"," 30700K .......... .......... .......... .......... .......... 53%  253M 0s\n"," 30750K .......... .......... .......... .......... .......... 53%  227M 0s\n"," 30800K .......... .......... .......... .......... .......... 54%  201M 0s\n"," 30850K .......... .......... .......... .......... .......... 54%  287M 0s\n"," 30900K .......... .......... .......... .......... .......... 54%  376M 0s\n"," 30950K .......... .......... .......... .......... .......... 54%  327M 0s\n"," 31000K .......... .......... .......... .......... .......... 54%  296M 0s\n"," 31050K .......... .......... .......... .......... .......... 54%  410M 0s\n"," 31100K .......... .......... .......... .......... .......... 54%  389M 0s\n"," 31150K .......... .......... .......... .......... .......... 54%  147M 0s\n"," 31200K .......... .......... .......... .......... .......... 54%  213M 0s\n"," 31250K .......... .......... .......... .......... .......... 54%  189M 0s\n"," 31300K .......... .......... .......... .......... .......... 54%  147M 0s\n"," 31350K .......... .......... .......... .......... .......... 54%  178M 0s\n"," 31400K .......... .......... .......... .......... .......... 55%  181M 0s\n"," 31450K .......... .......... .......... .......... .......... 55%  240M 0s\n"," 31500K .......... .......... .......... .......... .......... 55%  157M 0s\n"," 31550K .......... .......... .......... .......... .......... 55%  167M 0s\n"," 31600K .......... .......... .......... .......... .......... 55%  233M 0s\n"," 31650K .......... .......... .......... .......... .......... 55%  207M 0s\n"," 31700K .......... .......... .......... .......... .......... 55%  144M 0s\n"," 31750K .......... .......... .......... .......... .......... 55%  193M 0s\n"," 31800K .......... .......... .......... .......... .......... 55%  218M 0s\n"," 31850K .......... .......... .......... .......... .......... 55%  198M 0s\n"," 31900K .......... .......... .......... .......... .......... 55%  242M 0s\n"," 31950K .......... .......... .......... .......... .......... 56%  184M 0s\n"," 32000K .......... .......... .......... .......... .......... 56%  166M 0s\n"," 32050K .......... .......... .......... .......... .......... 56%  175M 0s\n"," 32100K .......... .......... .......... .......... .......... 56%  202M 0s\n"," 32150K .......... .......... .......... .......... .......... 56%  231M 0s\n"," 32200K .......... .......... .......... .......... .......... 56%  243M 0s\n"," 32250K .......... .......... .......... .......... .......... 56%  171M 0s\n"," 32300K .......... .......... .......... .......... .......... 56%  175M 0s\n"," 32350K .......... .......... .......... .......... .......... 56%  228M 0s\n"," 32400K .......... .......... .......... .......... .......... 56%  283M 0s\n"," 32450K .......... .......... .......... .......... .......... 56%  280M 0s\n"," 32500K .......... .......... .......... .......... .......... 57%  215M 0s\n"," 32550K .......... .......... .......... .......... .......... 57%  151M 0s\n"," 32600K .......... .......... .......... .......... .......... 57%  216M 0s\n"," 32650K .......... .......... .......... .......... .......... 57%  241M 0s\n"," 32700K .......... .......... .......... .......... .......... 57%  168M 0s\n"," 32750K .......... .......... .......... .......... .......... 57%  135M 0s\n"," 32800K .......... .......... .......... .......... .......... 57%  195M 0s\n"," 32850K .......... .......... .......... .......... .......... 57%  233M 0s\n"," 32900K .......... .......... .......... .......... .......... 57%  231M 0s\n"," 32950K .......... .......... .......... .......... .......... 57%  214M 0s\n"," 33000K .......... .......... .......... .......... .......... 57%  249M 0s\n"," 33050K .......... .......... .......... .......... .......... 57%  252M 0s\n"," 33100K .......... .......... .......... .......... .......... 58%  265M 0s\n"," 33150K .......... .......... .......... .......... .......... 58%  146M 0s\n"," 33200K .......... .......... .......... .......... .......... 58%  195M 0s\n"," 33250K .......... .......... .......... .......... .......... 58%  182M 0s\n"," 33300K .......... .......... .......... .......... .......... 58%  209M 0s\n"," 33350K .......... .......... .......... .......... .......... 58%  173M 0s\n"," 33400K .......... .......... .......... .......... .......... 58%  175M 0s\n"," 33450K .......... .......... .......... .......... .......... 58%  226M 0s\n"," 33500K .......... .......... .......... .......... .......... 58%  150M 0s\n"," 33550K .......... .......... .......... .......... .......... 58%  139M 0s\n"," 33600K .......... .......... .......... .......... .......... 58%  229M 0s\n"," 33650K .......... .......... .......... .......... .......... 59%  215M 0s\n"," 33700K .......... .......... .......... .......... .......... 59%  185M 0s\n"," 33750K .......... .......... .......... .......... .......... 59%  161M 0s\n"," 33800K .......... .......... .......... .......... .......... 59%  258M 0s\n"," 33850K .......... .......... .......... .......... .......... 59%  208M 0s\n"," 33900K .......... .......... .......... .......... .......... 59%  226M 0s\n"," 33950K .......... .......... .......... .......... .......... 59%  153M 0s\n"," 34000K .......... .......... .......... .......... .......... 59%  200M 0s\n"," 34050K .......... .......... .......... .......... .......... 59%  203M 0s\n"," 34100K .......... .......... .......... .......... .......... 59%  215M 0s\n"," 34150K .......... .......... .......... .......... .......... 59%  137M 0s\n"," 34200K .......... .......... .......... .......... .......... 59%  198M 0s\n"," 34250K .......... .......... .......... .......... .......... 60%  209M 0s\n"," 34300K .......... .......... .......... .......... .......... 60%  244M 0s\n"," 34350K .......... .......... .......... .......... .......... 60%  157M 0s\n"," 34400K .......... .......... .......... .......... .......... 60%  166M 0s\n"," 34450K .......... .......... .......... .......... .......... 60%  187M 0s\n"," 34500K .......... .......... .......... .......... .......... 60%  222M 0s\n"," 34550K .......... .......... .......... .......... .......... 60%  228M 0s\n"," 34600K .......... .......... .......... .......... .......... 60%  222M 0s\n"," 34650K .......... .......... .......... .......... .......... 60%  194M 0s\n"," 34700K .......... .......... .......... .......... .......... 60%  188M 0s\n"," 34750K .......... .......... .......... .......... .......... 60%  214M 0s\n"," 34800K .......... .......... .......... .......... .......... 61%  210M 0s\n"," 34850K .......... .......... .......... .......... .......... 61%  179M 0s\n"," 34900K .......... .......... .......... .......... .......... 61%  173M 0s\n"," 34950K .......... .......... .......... .......... .......... 61%  191M 0s\n"," 35000K .......... .......... .......... .......... .......... 61%  247M 0s\n"," 35050K .......... .......... .......... .......... .......... 61%  254M 0s\n"," 35100K .......... .......... .......... .......... .......... 61%  201M 0s\n"," 35150K .......... .......... .......... .......... .......... 61%  194M 0s\n"," 35200K .......... .......... .......... .......... .......... 61%  192M 0s\n"," 35250K .......... .......... .......... .......... .......... 61%  199M 0s\n"," 35300K .......... .......... .......... .......... .......... 61%  195M 0s\n"," 35350K .......... .......... .......... .......... .......... 61%  164M 0s\n"," 35400K .......... .......... .......... .......... .......... 62%  191M 0s\n"," 35450K .......... .......... .......... .......... .......... 62%  173M 0s\n"," 35500K .......... .......... .......... .......... .......... 62%  174M 0s\n"," 35550K .......... .......... .......... .......... .......... 62%  202M 0s\n"," 35600K .......... .......... .......... .......... .......... 62%  197M 0s\n"," 35650K .......... .......... .......... .......... .......... 62%  156M 0s\n"," 35700K .......... .......... .......... .......... .......... 62%  163M 0s\n"," 35750K .......... .......... .......... .......... .......... 62%  181M 0s\n"," 35800K .......... .......... .......... .......... .......... 62%  238M 0s\n"," 35850K .......... .......... .......... .......... .......... 62%  262M 0s\n"," 35900K .......... .......... .......... .......... .......... 62%  204M 0s\n"," 35950K .......... .......... .......... .......... .......... 63%  130M 0s\n"," 36000K .......... .......... .......... .......... .......... 63%  180M 0s\n"," 36050K .......... .......... .......... .......... .......... 63% 49.6M 0s\n"," 36100K .......... .......... .......... .......... .......... 63%  223M 0s\n"," 36150K .......... .......... .......... .......... .......... 63%  171M 0s\n"," 36200K .......... .......... .......... .......... .......... 63%  247M 0s\n"," 36250K .......... .......... .......... .......... .......... 63%  198M 0s\n"," 36300K .......... .......... .......... .......... .......... 63%  170M 0s\n"," 36350K .......... .......... .......... .......... .......... 63%  149M 0s\n"," 36400K .......... .......... .......... .......... .......... 63%  189M 0s\n"," 36450K .......... .......... .......... .......... .......... 63%  182M 0s\n"," 36500K .......... .......... .......... .......... .......... 64%  234M 0s\n"," 36550K .......... .......... .......... .......... .......... 64%  229M 0s\n"," 36600K .......... .......... .......... .......... .......... 64%  195M 0s\n"," 36650K .......... .......... .......... .......... .......... 64%  259M 0s\n"," 36700K .......... .......... .......... .......... .......... 64%  258M 0s\n"," 36750K .......... .......... .......... .......... .......... 64%  142M 0s\n"," 36800K .......... .......... .......... .......... .......... 64%  184M 0s\n"," 36850K .......... .......... .......... .......... .......... 64%  164M 0s\n"," 36900K .......... .......... .......... .......... .......... 64%  232M 0s\n"," 36950K .......... .......... .......... .......... .......... 64%  225M 0s\n"," 37000K .......... .......... .......... .......... .......... 64%  260M 0s\n"," 37050K .......... .......... .......... .......... .......... 64%  250M 0s\n"," 37100K .......... .......... .......... .......... .......... 65%  257M 0s\n"," 37150K .......... .......... .......... .......... .......... 65%  142M 0s\n"," 37200K .......... .......... .......... .......... .......... 65%  218M 0s\n"," 37250K .......... .......... .......... .......... .......... 65%  186M 0s\n"," 37300K .......... .......... .......... .......... .......... 65%  161M 0s\n"," 37350K .......... .......... .......... .......... .......... 65%  149M 0s\n"," 37400K .......... .......... .......... .......... .......... 65%  199M 0s\n"," 37450K .......... .......... .......... .......... .......... 65%  174M 0s\n"," 37500K .......... .......... .......... .......... .......... 65%  180M 0s\n"," 37550K .......... .......... .......... .......... .......... 65%  138M 0s\n"," 37600K .......... .......... .......... .......... .......... 65%  157M 0s\n"," 37650K .......... .......... .......... .......... .......... 66%  177M 0s\n"," 37700K .......... .......... .......... .......... .......... 66%  249M 0s\n"," 37750K .......... .......... .......... .......... .......... 66%  226M 0s\n"," 37800K .......... .......... .......... .......... .......... 66%  176M 0s\n"," 37850K .......... .......... .......... .......... .......... 66%  158M 0s\n"," 37900K .......... .......... .......... .......... .......... 66%  205M 0s\n"," 37950K .......... .......... .......... .......... .......... 66%  171M 0s\n"," 38000K .......... .......... .......... .......... .......... 66%  222M 0s\n"," 38050K .......... .......... .......... .......... .......... 66%  229M 0s\n"," 38100K .......... .......... .......... .......... .......... 66%  260M 0s\n"," 38150K .......... .......... .......... .......... .......... 66%  172M 0s\n"," 38200K .......... .......... .......... .......... .......... 66%  219M 0s\n"," 38250K .......... .......... .......... .......... .......... 67%  172M 0s\n"," 38300K .......... .......... .......... .......... .......... 67%  203M 0s\n"," 38350K .......... .......... .......... .......... .......... 67%  215M 0s\n"," 38400K .......... .......... .......... .......... .......... 67%  194M 0s\n"," 38450K .......... .......... .......... .......... .......... 67%  264M 0s\n"," 38500K .......... .......... .......... .......... .......... 67%  150M 0s\n"," 38550K .......... .......... .......... .......... .......... 67%  147M 0s\n"," 38600K .......... .......... .......... .......... .......... 67%  191M 0s\n"," 38650K .......... .......... .......... .......... .......... 67%  184M 0s\n"," 38700K .......... .......... .......... .......... .......... 67%  176M 0s\n"," 38750K .......... .......... .......... .......... .......... 67%  210M 0s\n"," 38800K .......... .......... .......... .......... .......... 68%  183M 0s\n"," 38850K .......... .......... .......... .......... .......... 68%  207M 0s\n"," 38900K .......... .......... .......... .......... .......... 68%  168M 0s\n"," 38950K .......... .......... .......... .......... .......... 68%  178M 0s\n"," 39000K .......... .......... .......... .......... .......... 68%  201M 0s\n"," 39050K .......... .......... .......... .......... .......... 68%  218M 0s\n"," 39100K .......... .......... .......... .......... .......... 68%  280M 0s\n"," 39150K .......... .......... .......... .......... .......... 68%  173M 0s\n"," 39200K .......... .......... .......... .......... .......... 68%  228M 0s\n"," 39250K .......... .......... .......... .......... .......... 68%  181M 0s\n"," 39300K .......... .......... .......... .......... .......... 68%  180M 0s\n"," 39350K .......... .......... .......... .......... .......... 69%  213M 0s\n"," 39400K .......... .......... .......... .......... .......... 69%  245M 0s\n"," 39450K .......... .......... .......... .......... .......... 69%  180M 0s\n"," 39500K .......... .......... .......... .......... .......... 69%  277M 0s\n"," 39550K .......... .......... .......... .......... .......... 69%  149M 0s\n"," 39600K .......... .......... .......... .......... .......... 69%  170M 0s\n"," 39650K .......... .......... .......... .......... .......... 69%  252M 0s\n"," 39700K .......... .......... .......... .......... .......... 69%  191M 0s\n"," 39750K .......... .......... .......... .......... .......... 69%  252M 0s\n"," 39800K .......... .......... .......... .......... .......... 69%  278M 0s\n"," 39850K .......... .......... .......... .......... .......... 69%  281M 0s\n"," 39900K .......... .......... .......... .......... .......... 69%  235M 0s\n"," 39950K .......... .......... .......... .......... .......... 70%  193M 0s\n"," 40000K .......... .......... .......... .......... .......... 70%  369M 0s\n"," 40050K .......... .......... .......... .......... .......... 70%  302M 0s\n"," 40100K .......... .......... .......... .......... .......... 70%  321M 0s\n"," 40150K .......... .......... .......... .......... .......... 70%  300M 0s\n"," 40200K .......... .......... .......... .......... .......... 70%  312M 0s\n"," 40250K .......... .......... .......... .......... .......... 70%  311M 0s\n"," 40300K .......... .......... .......... .......... .......... 70%  318M 0s\n"," 40350K .......... .......... .......... .......... .......... 70%  230M 0s\n"," 40400K .......... .......... .......... .......... .......... 70%  155M 0s\n"," 40450K .......... .......... .......... .......... .......... 70%  168M 0s\n"," 40500K .......... .......... .......... .......... .......... 71%  216M 0s\n"," 40550K .......... .......... .......... .......... .......... 71%  220M 0s\n"," 40600K .......... .......... .......... .......... .......... 71%  329M 0s\n"," 40650K .......... .......... .......... .......... .......... 71%  446M 0s\n"," 40700K .......... .......... .......... .......... .......... 71%  177M 0s\n"," 40750K .......... .......... .......... .......... .......... 71%  165M 0s\n"," 40800K .......... .......... .......... .......... .......... 71%  193M 0s\n"," 40850K .......... .......... .......... .......... .......... 71%  154M 0s\n"," 40900K .......... .......... .......... .......... .......... 71%  173M 0s\n"," 40950K .......... .......... .......... .......... .......... 71%  212M 0s\n"," 41000K .......... .......... .......... .......... .......... 71%  258M 0s\n"," 41050K .......... .......... .......... .......... .......... 71%  262M 0s\n"," 41100K .......... .......... .......... .......... .......... 72%  192M 0s\n"," 41150K .......... .......... .......... .......... .......... 72%  197M 0s\n"," 41200K .......... .......... .......... .......... .......... 72%  221M 0s\n"," 41250K .......... .......... .......... .......... .......... 72%  300M 0s\n"," 41300K .......... .......... .......... .......... .......... 72%  224M 0s\n"," 41350K .......... .......... .......... .......... .......... 72%  186M 0s\n"," 41400K .......... .......... .......... .......... .......... 72%  300M 0s\n"," 41450K .......... .......... .......... .......... .......... 72%  271M 0s\n"," 41500K .......... .......... .......... .......... .......... 72%  299M 0s\n"," 41550K .......... .......... .......... .......... .......... 72%  154M 0s\n"," 41600K .......... .......... .......... .......... .......... 72%  233M 0s\n"," 41650K .......... .......... .......... .......... .......... 73%  220M 0s\n"," 41700K .......... .......... .......... .......... .......... 73%  247M 0s\n"," 41750K .......... .......... .......... .......... .......... 73%  210M 0s\n"," 41800K .......... .......... .......... .......... .......... 73%  211M 0s\n"," 41850K .......... .......... .......... .......... .......... 73%  244M 0s\n"," 41900K .......... .......... .......... .......... .......... 73%  279M 0s\n"," 41950K .......... .......... .......... .......... .......... 73%  203M 0s\n"," 42000K .......... .......... .......... .......... .......... 73%  225M 0s\n"," 42050K .......... .......... .......... .......... .......... 73%  302M 0s\n"," 42100K .......... .......... .......... .......... .......... 73%  191M 0s\n"," 42150K .......... .......... .......... .......... .......... 73%  212M 0s\n"," 42200K .......... .......... .......... .......... .......... 73%  298M 0s\n"," 42250K .......... .......... .......... .......... .......... 74%  160M 0s\n"," 42300K .......... .......... .......... .......... .......... 74% 54.7M 0s\n"," 42350K .......... .......... .......... .......... .......... 74% 49.9M 0s\n"," 42400K .......... .......... .......... .......... .......... 74% 60.8M 0s\n"," 42450K .......... .......... .......... .......... .......... 74% 54.6M 0s\n"," 42500K .......... .......... .......... .......... .......... 74% 79.4M 0s\n"," 42550K .......... .......... .......... .......... .......... 74% 51.6M 0s\n"," 42600K .......... .......... .......... .......... .......... 74% 56.0M 0s\n"," 42650K .......... .......... .......... .......... .......... 74% 55.0M 0s\n"," 42700K .......... .......... .......... .......... .......... 74% 90.8M 0s\n"," 42750K .......... .......... .......... .......... .......... 74%  152M 0s\n"," 42800K .......... .......... .......... .......... .......... 75%  188M 0s\n"," 42850K .......... .......... .......... .......... .......... 75%  202M 0s\n"," 42900K .......... .......... .......... .......... .......... 75%  199M 0s\n"," 42950K .......... .......... .......... .......... .......... 75%  161M 0s\n"," 43000K .......... .......... .......... .......... .......... 75%  208M 0s\n"," 43050K .......... .......... .......... .......... .......... 75%  168M 0s\n"," 43100K .......... .......... .......... .......... .......... 75%  187M 0s\n"," 43150K .......... .......... .......... .......... .......... 75%  123M 0s\n"," 43200K .......... .......... .......... .......... .......... 75%  202M 0s\n"," 43250K .......... .......... .......... .......... .......... 75%  248M 0s\n"," 43300K .......... .......... .......... .......... .......... 75%  262M 0s\n"," 43350K .......... .......... .......... .......... .......... 76%  213M 0s\n"," 43400K .......... .......... .......... .......... .......... 76%  237M 0s\n"," 43450K .......... .......... .......... .......... .......... 76%  205M 0s\n"," 43500K .......... .......... .......... .......... .......... 76%  263M 0s\n"," 43550K .......... .......... .......... .......... .......... 76%  159M 0s\n"," 43600K .......... .......... .......... .......... .......... 76%  170M 0s\n"," 43650K .......... .......... .......... .......... .......... 76%  175M 0s\n"," 43700K .......... .......... .......... .......... .......... 76%  253M 0s\n"," 43750K .......... .......... .......... .......... .......... 76%  229M 0s\n"," 43800K .......... .......... .......... .......... .......... 76%  187M 0s\n"," 43850K .......... .......... .......... .......... .......... 76%  248M 0s\n"," 43900K .......... .......... .......... .......... .......... 76%  264M 0s\n"," 43950K .......... .......... .......... .......... .......... 77%  177M 0s\n"," 44000K .......... .......... .......... .......... .......... 77%  236M 0s\n"," 44050K .......... .......... .......... .......... .......... 77%  213M 0s\n"," 44100K .......... .......... .......... .......... .......... 77%  252M 0s\n"," 44150K .......... .......... .......... .......... .......... 77%  165M 0s\n"," 44200K .......... .......... .......... .......... .......... 77%  277M 0s\n"," 44250K .......... .......... .......... .......... .......... 77%  253M 0s\n"," 44300K .......... .......... .......... .......... .......... 77%  178M 0s\n"," 44350K .......... .......... .......... .......... .......... 77%  167M 0s\n"," 44400K .......... .......... .......... .......... .......... 77%  221M 0s\n"," 44450K .......... .......... .......... .......... .......... 77%  245M 0s\n"," 44500K .......... .......... .......... .......... .......... 78%  187M 0s\n"," 44550K .......... .......... .......... .......... .......... 78%  168M 0s\n"," 44600K .......... .......... .......... .......... .......... 78%  242M 0s\n"," 44650K .......... .......... .......... .......... .......... 78%  230M 0s\n"," 44700K .......... .......... .......... .......... .......... 78%  260M 0s\n"," 44750K .......... .......... .......... .......... .......... 78%  162M 0s\n"," 44800K .......... .......... .......... .......... .......... 78%  250M 0s\n"," 44850K .......... .......... .......... .......... .......... 78%  227M 0s\n"," 44900K .......... .......... .......... .......... .......... 78%  189M 0s\n"," 44950K .......... .......... .......... .......... .......... 78%  155M 0s\n"," 45000K .......... .......... .......... .......... .......... 78%  166M 0s\n"," 45050K .......... .......... .......... .......... .......... 78%  240M 0s\n"," 45100K .......... .......... .......... .......... .......... 79%  220M 0s\n"," 45150K .......... .......... .......... .......... .......... 79%  207M 0s\n"," 45200K .......... .......... .......... .......... .......... 79%  258M 0s\n"," 45250K .......... .......... .......... .......... .......... 79%  257M 0s\n"," 45300K .......... .......... .......... .......... .......... 79%  234M 0s\n"," 45350K .......... .......... .......... .......... .......... 79%  230M 0s\n"," 45400K .......... .......... .......... .......... .......... 79%  259M 0s\n"," 45450K .......... .......... .......... .......... .......... 79%  268M 0s\n"," 45500K .......... .......... .......... .......... .......... 79%  267M 0s\n"," 45550K .......... .......... .......... .......... .......... 79%  207M 0s\n"," 45600K .......... .......... .......... .......... .......... 79%  255M 0s\n"," 45650K .......... .......... .......... .......... .......... 80%  250M 0s\n"," 45700K .......... .......... .......... .......... .......... 80%  268M 0s\n"," 45750K .......... .......... .......... .......... .......... 80%  231M 0s\n"," 45800K .......... .......... .......... .......... .......... 80%  239M 0s\n"," 45850K .......... .......... .......... .......... .......... 80%  260M 0s\n"," 45900K .......... .......... .......... .......... .......... 80%  214M 0s\n"," 45950K .......... .......... .......... .......... .......... 80%  142M 0s\n"," 46000K .......... .......... .......... .......... .......... 80%  174M 0s\n"," 46050K .......... .......... .......... .......... .......... 80%  208M 0s\n"," 46100K .......... .......... .......... .......... .......... 80%  209M 0s\n"," 46150K .......... .......... .......... .......... .......... 80%  179M 0s\n"," 46200K .......... .......... .......... .......... .......... 81%  162M 0s\n"," 46250K .......... .......... .......... .......... .......... 81%  220M 0s\n"," 46300K .......... .......... .......... .......... .......... 81%  172M 0s\n"," 46350K .......... .......... .......... .......... .......... 81%  182M 0s\n"," 46400K .......... .......... .......... .......... .......... 81%  158M 0s\n"," 46450K .......... .......... .......... .......... .......... 81%  204M 0s\n"," 46500K .......... .......... .......... .......... .......... 81%  208M 0s\n"," 46550K .......... .......... .......... .......... .......... 81%  159M 0s\n"," 46600K .......... .......... .......... .......... .......... 81%  204M 0s\n"," 46650K .......... .......... .......... .......... .......... 81%  194M 0s\n"," 46700K .......... .......... .......... .......... .......... 81%  179M 0s\n"," 46750K .......... .......... .......... .......... .......... 81%  149M 0s\n"," 46800K .......... .......... .......... .......... .......... 82%  226M 0s\n"," 46850K .......... .......... .......... .......... .......... 82%  192M 0s\n"," 46900K .......... .......... .......... .......... .......... 82%  191M 0s\n"," 46950K .......... .......... .......... .......... .......... 82%  153M 0s\n"," 47000K .......... .......... .......... .......... .......... 82%  202M 0s\n"," 47050K .......... .......... .......... .......... .......... 82%  199M 0s\n"," 47100K .......... .......... .......... .......... .......... 82%  194M 0s\n"," 47150K .......... .......... .......... .......... .......... 82%  157M 0s\n"," 47200K .......... .......... .......... .......... .......... 82%  168M 0s\n"," 47250K .......... .......... .......... .......... .......... 82%  234M 0s\n"," 47300K .......... .......... .......... 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47950K .......... .......... .......... .......... .......... 84%  223M 0s\n"," 48000K .......... .......... .......... .......... .......... 84%  268M 0s\n"," 48050K .......... .......... .......... .......... .......... 84%  265M 0s\n"," 48100K .......... .......... .......... .......... .......... 84%  135M 0s\n"," 48150K .......... .......... .......... .......... .......... 84%  158M 0s\n"," 48200K .......... .......... .......... .......... .......... 84%  185M 0s\n"," 48250K .......... .......... .......... .......... .......... 84% 96.1M 0s\n"," 48300K .......... .......... .......... .......... .......... 84%  196M 0s\n"," 48350K .......... .......... .......... .......... .......... 84%  122M 0s\n"," 48400K .......... .......... .......... .......... .......... 84%  197M 0s\n"," 48450K .......... .......... .......... .......... .......... 84%  197M 0s\n"," 48500K .......... .......... .......... .......... .......... 85%  201M 0s\n"," 48550K .......... .......... .......... 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"]}],"source":["!python -m pip install --upgrade pip setuptools\n","!pip uninstall tensorflow\n","!pip install tensorflow-gpu==1.15.5"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"g0I_AzjK0mg8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1691781030929,"user_tz":420,"elapsed":7884,"user":{"displayName":"Siyuan Shan","userId":"16492559305772335155"}},"outputId":"c80012f2-874f-4be0-97ef-b4fbe67e0ec4"},"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:From <ipython-input-4-4a3f57d5652e>:2: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.config.list_physical_devices('GPU')` instead.\n"]},{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":4}],"source":["import tensorflow as tf\n","tf.test.is_gpu_available()"]},{"cell_type":"markdown","source":["Clone our Phoneme Hallucinator repo and download our model checkpoints"],"metadata":{"id":"hRguoKXUP52q"}},{"cell_type":"code","execution_count":5,"metadata":{"id":"LOQYA5043rkf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1691781049674,"user_tz":420,"elapsed":16133,"user":{"displayName":"Siyuan Shan","userId":"16492559305772335155"}},"outputId":"c5a19b95-9dc3-4a9c-bb93-418a5aa99678"},"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'Phoneme_Hallucinator'...\n","remote: Enumerating objects: 83, done.\u001b[K\n","remote: Counting objects: 100% (83/83), done.\u001b[K\n","remote: Compressing objects: 100% (78/78), done.\u001b[K\n","remote: Total 83 (delta 19), reused 52 (delta 1), pack-reused 0\u001b[K\n","Receiving objects: 100% (83/83), 798.20 KiB | 12.47 MiB/s, done.\n","Resolving deltas: 100% (19/19), done.\n","--2023-08-11 19:10:33--  https://www.dropbox.com/scl/fi/ytj3mwkf1fd0no4jtg7r7/weights.zip?rlkey=ilyxue0gpuppyzn6u01bbjiy9\n","Resolving www.dropbox.com (www.dropbox.com)... 162.125.2.18, 2620:100:601d:18::a27d:512\n","Connecting to www.dropbox.com (www.dropbox.com)|162.125.2.18|:443... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://www.dropbox.com/e/scl/fi/ytj3mwkf1fd0no4jtg7r7/weights.zip?rlkey=ilyxue0gpuppyzn6u01bbjiy9 [following]\n","--2023-08-11 19:10:33--  https://www.dropbox.com/e/scl/fi/ytj3mwkf1fd0no4jtg7r7/weights.zip?rlkey=ilyxue0gpuppyzn6u01bbjiy9\n","Reusing existing connection to www.dropbox.com:443.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://ucef15a120ef9aa8e921316cbc6a.dl.dropboxusercontent.com/cd/0/get/CBm7lBCcfpcrSwGN5gINKui5NUmv9L8GTFjbL1w1HZohT53acTFqAWfALzgItMDdib0KArpTAwK7gA93_NKJF1NaToprAtC7hwtJUfIwyCFqOz3w78d0i51uP5me-meHUnR5hnrgMbWO3pN4FJGw7Pnh/file# [following]\n","--2023-08-11 19:10:34--  https://ucef15a120ef9aa8e921316cbc6a.dl.dropboxusercontent.com/cd/0/get/CBm7lBCcfpcrSwGN5gINKui5NUmv9L8GTFjbL1w1HZohT53acTFqAWfALzgItMDdib0KArpTAwK7gA93_NKJF1NaToprAtC7hwtJUfIwyCFqOz3w78d0i51uP5me-meHUnR5hnrgMbWO3pN4FJGw7Pnh/file\n","Resolving ucef15a120ef9aa8e921316cbc6a.dl.dropboxusercontent.com (ucef15a120ef9aa8e921316cbc6a.dl.dropboxusercontent.com)... 162.125.5.15, 2620:100:601d:15::a27d:50f\n","Connecting to ucef15a120ef9aa8e921316cbc6a.dl.dropboxusercontent.com (ucef15a120ef9aa8e921316cbc6a.dl.dropboxusercontent.com)|162.125.5.15|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 389589880 (372M) [application/binary]\n","Saving to: ‘/content/weights.zip’\n","\n","/content/weights.zi 100%[===================>] 371.54M  55.0MB/s    in 6.9s    \n","\n","2023-08-11 19:10:42 (54.0 MB/s) - ‘/content/weights.zip’ saved [389589880/389589880]\n","\n","Archive:  /content/weights.zip\n","   creating: weights/\n","  inflating: weights/checkpoint      \n","  inflating: weights/params.ckpt.index  \n","  inflating: weights/last.ckpt.index  \n","  inflating: weights/last.ckpt.meta  \n","  inflating: weights/params.ckpt.data-00000-of-00001  \n","  inflating: weights/params.ckpt.meta  \n","  inflating: weights/last.ckpt.data-00000-of-00001  \n"]}],"source":["!rm -r -f ./Phoneme_Hallucinator\n","!git clone https://github.com/PhonemeHallucinator/Phoneme_Hallucinator.git\n","!wget -O /content/weights.zip https://www.dropbox.com/scl/fi/ytj3mwkf1fd0no4jtg7r7/weights.zip?rlkey=ilyxue0gpuppyzn6u01bbjiy9&dl=1\n","!unzip /content/weights.zip\n","!mv /content/weights /content/Phoneme_Hallucinator/exp/speech_XXL_cond/"]},{"cell_type":"markdown","source":["Reuse the kNN-VC codes to extract speech representations for both source audio and target audio"],"metadata":{"id":"ujncgSHKQKTo"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"RAgVJPhw1Rmw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1691781120533,"user_tz":420,"elapsed":14903,"user":{"displayName":"Siyuan Shan","userId":"16492559305772335155"}},"outputId":"33c13085-001d-4eff-a971-966782d712a0"},"outputs":[{"output_type":"stream","name":"stderr","text":["Using cache found in /root/.cache/torch/hub/bshall_knn-vc_master\n"]},{"output_type":"stream","name":"stdout","text":["Removing weight norm...\n","[HiFiGAN] Generator loaded with 16,523,393 parameters.\n","WavLM-Large loaded with 315,453,120 parameters.\n"]}],"source":["import torch, torchaudio\n","knn_vc = torch.hub.load('bshall/knn-vc', 'knn_vc', prematched=True, trust_repo=True, pretrained=True, device='cuda')\n","# path to 16kHz, single-channel, source waveform\n","src_wav_path = '/content/Phoneme_Hallucinator/audio/source.wav'\n","# list of paths to all reference waveforms (each must be 16kHz, single-channel) from the target speaker\n","ref_wav_paths = ['/content/Phoneme_Hallucinator/audio/target.wav', ]\n","src_audio, sr = torchaudio.load(src_wav_path)\n","\n","ref_audio, sr = torchaudio.load(ref_wav_paths[0])\n","\n","query_seq = knn_vc.get_features(src_wav_path)\n","matching_set = knn_vc.get_matching_set(ref_wav_paths)\n","unexpanded_out_wav = knn_vc.match(query_seq, matching_set, topk=4)\n","\n","torch.save(matching_set, \"matching_set.pt\")\n"]},{"cell_type":"markdown","source":["Install packages required by phoneme hallucinator"],"metadata":{"id":"2w69hbV4QWtJ"}},{"cell_type":"code","execution_count":2,"metadata":{"id":"uQ27tOEHGIYq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1691781265328,"user_tz":420,"elapsed":92632,"user":{"displayName":"Siyuan Shan","userId":"16492559305772335155"}},"outputId":"e0ac63ea-8273-4026-e85b-ddc435684864"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting torch\n","  Downloading torch-1.10.2-cp36-cp36m-manylinux1_x86_64.whl (881.9 MB)\n","     |████████████████████████████████| 881.9 MB 7.4 kB/s             \n","\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.6/site-packages (from torch) (4.1.1)\n","Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/site-packages (from torch) (0.8)\n","Installing collected packages: torch\n","Successfully installed torch-1.10.2\n","\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. 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You can change how many new representations to hallucinate by changing \"--num_samples\"."],"metadata":{"id":"GaX6ktHiQbHQ"}},{"cell_type":"code","execution_count":3,"metadata":{"id":"fJEcRAP10rKw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1691781343736,"user_tz":420,"elapsed":76056,"user":{"displayName":"Siyuan Shan","userId":"16492559305772335155"}},"outputId":"e366a2b9-cda3-4b4c-a6c1-61f72c5c66a7"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","2023-08-11 19:14:54.968610: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_3_pre/value/Tensordot/transpose_1_grad/transpose: (Transpose): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/concat_3_grad/mod: (FloorMod): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.968637: I 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/job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/concat_2_grad/Shape: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.969254: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/concat_2_grad/Shape: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/concat_2_grad/ShapeN: (ShapeN): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.969279: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/concat_2_grad/ShapeN: (ShapeN): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/concat_2_grad/ConcatOffset: (ConcatOffset): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.969323: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/concat_2_grad/ConcatOffset: (ConcatOffset): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/concat_2_grad/Slice: (Slice): 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(NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/stack_4_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.969517: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/stack_4_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/stack_4_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.969536: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/stack_4_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/self_attn_3_pre/query/Tensordot/Reshape_grad/Shape: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.969563: I tensorflow/core/common_runtime/placer.cc:54] 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gradients/acset_vae/transform_2/mul_10_grad/BroadcastGradientArgs: (BroadcastGradientArgs): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/mul_10_grad/Mul: (Mul): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.969894: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/mul_10_grad/Mul: (Mul): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/mul_10_grad/Sum: (Sum): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.969919: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/mul_10_grad/Sum: (Sum): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/mul_10_grad/Reshape: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.969944: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/mul_10_grad/Reshape: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/mul_10_grad/Mul_1: (Mul): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.969970: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/mul_10_grad/Mul_1: (Mul): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/mul_10_grad/Sum_1: (Sum): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.969995: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/mul_10_grad/Sum_1: (Sum): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/mul_10_grad/Reshape_1: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.970021: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/mul_10_grad/Reshape_1: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/mul_10_grad/tuple/group_deps: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.970045: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/mul_10_grad/tuple/group_deps: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/mul_10_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.970068: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/mul_10_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/mul_10_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.970091: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/mul_10_grad/tuple/control_dependency_1: (Identity): 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tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/add_24_grad/BroadcastGradientArgs: (BroadcastGradientArgs): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/add_24_grad/Sum: (Sum): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.970850: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/add_24_grad/Sum: (Sum): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/add_24_grad/Reshape: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.970875: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/add_24_grad/Reshape: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/add_24_grad/Sum_1: (Sum): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.970899: I tensorflow/core/common_runtime/placer.cc:54] 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(Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/add_24_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.970994: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/add_24_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/Exp_4_grad/mul: (Mul): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.971020: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/Exp_4_grad/mul: (Mul): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/Tile_3_grad/stack: (Pack): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.971046: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/Tile_3_grad/stack: (Pack): 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tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/Maximum_2_grad/zeros: (Fill): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/Maximum_2_grad/GreaterEqual: (GreaterEqual): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.976105: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/Maximum_2_grad/GreaterEqual: (GreaterEqual): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/Maximum_2_grad/BroadcastGradientArgs: (BroadcastGradientArgs): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.976131: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/Maximum_2_grad/BroadcastGradientArgs: (BroadcastGradientArgs): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/Maximum_2_grad/Select: (Select): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:54.976157: I 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gradients/acset_vae/transform_2/strided_slice_6_grad/Shape: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/strided_slice_6_grad/StridedSliceGrad: (StridedSliceGrad): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.215011: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/strided_slice_6_grad/StridedSliceGrad: (StridedSliceGrad): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/self_attn_1_post/output/Relu_grad/ReluGrad: (ReluGrad): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.215038: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_1_post/output/Relu_grad/ReluGrad: (ReluGrad): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/set_xformer/set_xformer/self_attn_2_post/output/BiasAdd_grad/BiasAddGrad: (BiasAddGrad): 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gradients/acset_vae/prior/set_xformer/self_attn_1_post/output/BiasAdd_grad/BiasAddGrad: (BiasAddGrad): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/self_attn_1_post/output/BiasAdd_grad/tuple/group_deps: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.215785: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_1_post/output/BiasAdd_grad/tuple/group_deps: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/self_attn_1_post/output/BiasAdd_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.215829: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_1_post/output/BiasAdd_grad/tuple/control_dependency: (Identity): 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(Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.218695: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_1_post/output/Tensordot_grad/Shape: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/self_attn_1_post/output/Tensordot_grad/Reshape: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.218726: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_1_post/output/Tensordot_grad/Reshape: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/set_xformer/set_xformer/self_attn_2_post/output/Tensordot/MatMul_grad/MatMul: (MatMul): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.218752: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/set_xformer/set_xformer/self_attn_2_post/output/Tensordot/MatMul_grad/MatMul: (MatMul): 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/job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform/lrelu_5/Sigmoid_grad/SigmoidGrad: (SigmoidGrad): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.219219: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform/lrelu_5/Sigmoid_grad/SigmoidGrad: (SigmoidGrad): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/AddN_53: (AddN): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.219250: I tensorflow/core/common_runtime/placer.cc:54] gradients/AddN_53: (AddN): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/add_11_grad/Shape: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.219277: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/add_11_grad/Shape: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/add_11_grad/Shape_1: (Shape): 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gradients/acset_vae/prior/set_xformer/self_attn_1_post/output/Tensordot/transpose_1_grad/InvertPermutation: (InvertPermutation): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/self_attn_1_post/output/Tensordot/transpose_1_grad/transpose: (Transpose): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.316711: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_1_post/output/Tensordot/transpose_1_grad/transpose: (Transpose): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/AddN_54: (AddN): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.316732: I tensorflow/core/common_runtime/placer.cc:54] gradients/AddN_54: (AddN): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/set_xformer/set_xformer/self_attn_2_post/concat_3_grad/mod: (FloorMod): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.316759: I 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tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/add_10_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/add_10_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.317762: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/add_10_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/AddN_56: (AddN): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.317784: I tensorflow/core/common_runtime/placer.cc:54] gradients/AddN_56: (AddN): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/MatrixBandPart_1_grad/MatrixBandPart: (MatrixBandPart): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.317806: I tensorflow/core/common_runtime/placer.cc:54] 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gradients/acset_vae/transform_2/coupling_2/ms2/l0/MatMul_grad/tuple/group_deps: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/coupling_2/ms2/l0/MatMul_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.436969: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/coupling_2/ms2/l0/MatMul_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/coupling_2/ms2/l0/MatMul_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.436991: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/coupling_2/ms2/l0/MatMul_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/self_attn_1_post/query/Tensordot/Reshape_grad/Shape: (Shape): 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tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_1_post/key/Tensordot/Reshape_1_grad/Reshape: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/set_xformer/set_xformer/self_attn_2_post/query/Tensordot/transpose_grad/InvertPermutation: (InvertPermutation): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.437180: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/set_xformer/set_xformer/self_attn_2_post/query/Tensordot/transpose_grad/InvertPermutation: (InvertPermutation): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/set_xformer/set_xformer/self_attn_2_post/query/Tensordot/transpose_grad/transpose: (Transpose): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.437207: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/set_xformer/set_xformer/self_attn_2_post/query/Tensordot/transpose_grad/transpose: (Transpose): 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gradients/acset_vae/prior/set_xformer/self_attn_1_post/query/Tensordot/transpose_grad/InvertPermutation: (InvertPermutation): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/self_attn_1_post/query/Tensordot/transpose_grad/transpose: (Transpose): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.437520: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_1_post/query/Tensordot/transpose_grad/transpose: (Transpose): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/self_attn_1_post/query/Tensordot/transpose_1_grad/InvertPermutation: (InvertPermutation): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.437545: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_1_post/query/Tensordot/transpose_1_grad/InvertPermutation: (InvertPermutation): 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gradients/acset_vae/prior/set_xformer/self_attn_1_post/key/Tensordot/transpose_1_grad/transpose: (Transpose): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/AddN_62: (AddN): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.437697: I tensorflow/core/common_runtime/placer.cc:54] gradients/AddN_62: (AddN): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/add_1_grad/Shape: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.437722: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/add_1_grad/Shape: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/add_1_grad/Shape_1: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.437748: I tensorflow/core/common_runtime/placer.cc:54] 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gradients/acset_vae/prior/set_xformer/self_attn_1_pre/output/Relu_grad/ReluGrad: (ReluGrad): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/output/BiasAdd_grad/BiasAddGrad: (BiasAddGrad): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.438555: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/output/BiasAdd_grad/BiasAddGrad: (BiasAddGrad): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/output/BiasAdd_grad/tuple/group_deps: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.438581: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/output/BiasAdd_grad/tuple/group_deps: (NoOp): 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gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/value/Tensordot/Reshape_grad/Reshape: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/value/Tensordot/Reshape_1_grad/Reshape: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.461003: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/value/Tensordot/Reshape_1_grad/Reshape: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/AddN_72: (AddN): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.461030: I tensorflow/core/common_runtime/placer.cc:54] gradients/AddN_72: (AddN): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform/lrelu_1/Sigmoid_grad/SigmoidGrad: (SigmoidGrad): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.461057: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform/lrelu_1/Sigmoid_grad/SigmoidGrad: (SigmoidGrad): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/AddN_73: (AddN): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.461078: I tensorflow/core/common_runtime/placer.cc:54] gradients/AddN_73: (AddN): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/add_1_grad/Shape: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.461103: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/add_1_grad/Shape: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/add_1_grad/Shape_1: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.461129: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/add_1_grad/Shape_1: (Shape): 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tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/value/Tensordot/transpose_grad/InvertPermutation: (InvertPermutation): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/value/Tensordot/transpose_grad/transpose: (Transpose): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.462220: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/value/Tensordot/transpose_grad/transpose: (Transpose): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/value/Tensordot/transpose_1_grad/InvertPermutation: (InvertPermutation): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.462276: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/set_xformer/set_xformer/self_attn_2_pre/value/Tensordot/transpose_1_grad/InvertPermutation: 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gradients/acset_vae/posterior/Normal/sample/add_grad/Reshape: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/Normal/sample/add_grad/Sum_1: (Sum): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.464307: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/Normal/sample/add_grad/Sum_1: (Sum): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/Normal/sample/add_grad/Reshape_1: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.464332: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/Normal/sample/add_grad/Reshape_1: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/Normal/sample/add_grad/tuple/group_deps: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.464406: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/Normal/sample/add_grad/tuple/group_deps: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/Normal/sample/add_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.464432: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/Normal/sample/add_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/Normal/sample/add_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.464454: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/Normal/sample/add_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/sub_grad/Neg: (Neg): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.464479: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/sub_grad/Neg: (Neg): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/sub_grad/tuple/group_deps: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.464619: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/sub_grad/tuple/group_deps: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/sub_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.464644: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/transform_2/sub_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/transform_2/sub_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.464666: I tensorflow/core/common_runtime/placer.cc:54] 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gradients/acset_vae/prior/set_xformer/self_attn_0_post/output/BiasAdd_grad/BiasAddGrad: (BiasAddGrad): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/self_attn_0_post/output/BiasAdd_grad/tuple/group_deps: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.468726: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_0_post/output/BiasAdd_grad/tuple/group_deps: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/self_attn_0_post/output/BiasAdd_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.468747: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_0_post/output/BiasAdd_grad/tuple/control_dependency: (Identity): 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19:14:55.482198: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_0_post/MatMul_grad/Shape_1: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/self_attn_0_post/MatMul_grad/strided_slice: (StridedSlice): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:14:55.482228: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_0_post/MatMul_grad/strided_slice: (StridedSlice): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/self_attn_0_post/MatMul_grad/strided_slice_1: (StridedSlice): /job:localhost/replica:0/task:0/device:CPU:0\n"]},{"output_type":"stream","name":"stderr","text":["IOPub data rate exceeded.\n","The notebook server will temporarily stop sending output\n","to the client in order to avoid crashing it.\n","To change this limit, set the config variable\n","`--NotebookApp.iopub_data_rate_limit`.\n","\n","Current values:\n","NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n","NotebookApp.rate_limit_window=3.0 (secs)\n","\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","2023-08-11 19:15:08.063129: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_0_post/concat_2_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/prior/set_xformer/self_attn_0_post/concat_2_grad/tuple/control_dependency_2: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.063151: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/prior/set_xformer/self_attn_0_post/concat_2_grad/tuple/control_dependency_2: (Identity): 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gradients/acset_vae/set_xformer/set_xformer/self_attn_0_post/value/Tensordot/transpose_1_grad/transpose: (Transpose): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_2_post/split_3_grad/concat: (ConcatV2): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.515914: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_2_post/split_3_grad/concat: (ConcatV2): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/set_xformer/set_xformer/self_attn_0_post/query/Tensordot/MatMul_grad/MatMul: (MatMul): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.515939: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/set_xformer/set_xformer/self_attn_0_post/query/Tensordot/MatMul_grad/MatMul: (MatMul): 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gradients/acset_vae/posterior/set_xformer/self_attn_0_post/output/Tensordot/transpose_1_grad/transpose: (Transpose): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/AddN_115: (AddN): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.863756: I tensorflow/core/common_runtime/placer.cc:54] gradients/AddN_115: (AddN): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_post/concat_3_grad/mod: (FloorMod): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.863781: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_post/concat_3_grad/mod: (FloorMod): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_post/concat_3_grad/Shape: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.863798: I tensorflow/core/common_runtime/placer.cc:54] 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tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/BroadcastGradientArgs: (BroadcastGradientArgs): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/RealDiv: (RealDiv): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.874805: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/RealDiv: (RealDiv): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/Sum: (Sum): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.875017: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/Sum: (Sum): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/Reshape: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.875065: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/Reshape: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/Neg: (Neg): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.875092: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/Neg: (Neg): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/RealDiv_1: (RealDiv): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.875118: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/RealDiv_1: (RealDiv): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/RealDiv_2: (RealDiv): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.875157: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/RealDiv_2: (RealDiv): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/mul: (Mul): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.875180: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/mul: (Mul): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/Sum_1: (Sum): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.875214: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/Sum_1: (Sum): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/Reshape_1: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.875235: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/Reshape_1: (Reshape): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/tuple/group_deps: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.875258: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/tuple/group_deps: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.875279: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/tuple/control_dependency: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.875313: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/truediv_grad/tuple/control_dependency_1: (Identity): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/split_2_grad/concat: (ConcatV2): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.875480: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/split_2_grad/concat: (ConcatV2): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/MatMul_grad/MatMul: (BatchMatMulV2): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.875525: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/MatMul_grad/MatMul: (BatchMatMulV2): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/MatMul_grad/MatMul_1: (BatchMatMulV2): /job:localhost/replica:0/task:0/device:CPU:0\n","2023-08-11 19:15:08.875548: I tensorflow/core/common_runtime/placer.cc:54] gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/MatMul_grad/MatMul_1: (BatchMatMulV2): /job:localhost/replica:0/task:0/device:CPU:0\n","gradients/acset_vae/posterior/set_xformer/self_attn_0_pre/MatMul_grad/Shape: (Shape): /job:localhost/replica:0/task:0/device:CPU:0\n"]},{"output_type":"stream","name":"stderr","text":["IOPub data rate exceeded.\n","The notebook server will temporarily stop sending output\n","to the client in order to avoid crashing it.\n","To change this limit, set the config variable\n","`--NotebookApp.iopub_data_rate_limit`.\n","\n","Current values:\n","NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n","NotebookApp.rate_limit_window=3.0 (secs)\n","\n"]},{"output_type":"stream","name":"stdout","text":["WARNING:tensorflow:From ./scripts/speech_expansion_ins.py:139: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.\n","\n","WARNING:tensorflow:From ./scripts/speech_expansion_ins.py:139: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.\n","\n","/content\n"]}],"source":["%cd Phoneme_Hallucinator\n","!python ./scripts/speech_expansion_ins.py --cfg_file=./exp/speech_XXL_cond/params.json --num_samples 15000 --path /content/matching_set.pt --out_path /content/expanded_set.npy\n","%cd ..\n","import numpy as np\n","import IPython\n","expanded_set = torch.tensor(np.load(\"expanded_set.npy\")).to(matching_set.device)\n","matching_set = torch.cat([matching_set, expanded_set], 0)\n","out_wav = knn_vc.match(query_seq, matching_set, topk=4)"]},{"cell_type":"markdown","source":["Listen to audio results. \"unexpanded_converted\" is the result of kNN-VC. \"converted\" is our result."],"metadata":{"id":"svOkD0T8Qmtw"}},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":319,"output_embedded_package_id":"1cviSYlo7H0U94eL9L1y5kHb1JvXP1gYz"},"id":"aEk63VDgLwSf","executionInfo":{"status":"ok","timestamp":1691781349569,"user_tz":420,"elapsed":2005,"user":{"displayName":"Siyuan Shan","userId":"16492559305772335155"}},"outputId":"9047b47c-ba97-4c55-8bf6-9f24d2516500"},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["import IPython.display as ipd\n","print(\"source\")\n","IPython.display.display(ipd.Audio(src_audio.numpy(), rate=sr))\n","print(\"target\")\n","IPython.display.display(ipd.Audio(ref_audio.numpy(), rate=sr))\n","print(\"unexpanded_converted\")\n","IPython.display.display(ipd.Audio(unexpanded_out_wav.numpy(), rate=16000))\n","print(\"converted\")\n","IPython.display.display(ipd.Audio(out_wav.numpy(), rate=16000))"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[],"authorship_tag":"ABX9TyPo/dxKRIipDId/lhYjcPrW"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/Phoneme_Hallucinator_v2/README.md b/Phoneme_Hallucinator_v2/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2710363f6a239de5c3e284fad9b401c7ea21c3c3
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/README.md
@@ -0,0 +1,36 @@
+# Phoneme_Hallucinator
+This is the repository of the paper "Phoneme Hallucinator: One-shot Voice Conversion via Set Expansion" accepted by AAAI-2024. Some audio samples are provided [here](https://phonemehallucinator.github.io/).
+
+## Inference Tutorial
+1. If you only want to run our VC pipeline, please download `Phoneme Hallucinator DEMO.ipynb` in this repo and run it in google colab.
+   
+## Training Tutorial
+1. Prepare environment. Require `Python 3.6.3` and the following packages
+   ```
+   pillow == 8.0.1
+   torch == 1.10.2
+   tensorflow == 1.15.5
+   tensorflow-probability == 0.7.0
+   tensorpack == 0.9.8
+   h5py == 2.10.0
+   numpy == 1.19.5
+   pathlib == 1.0.1
+   tqdm == 4.64.1
+   easydict == 1.10
+   matplotlib == 3.3.4
+   scikit-learn == 0.24.2
+   scipy == 1.5.4
+   seaborn == 0.11.2
+   ```
+3. To prepare the training set, we need to use WavLM to extract speech representations. Go to [kNN-VC repo](https://github.com/bshall/knn-vc) and follow its instructions to extract speech representations. Namely, after placing LibriSpeech dataset in a correct location, run the command:
+
+   `python prematch_dataset.py --librispeech_path /path/to/librispeech/root --out_path /path/where/you/want/outputs/to/go --topk 4 --matching_layer 6 --synthesis_layer 6`
+
+   Note that we don't use the "--prematch" option, becuase we only need to extract representations, not to extract and then perform kNN regression.
+
+4. After the above step, you can get a `--out_path` folder with three subfolders `train-clean-100`, `test-clean` and `dev-clean` where each folder contains the speech representation files (".pt").
+5. Go to our repo `./dataset/speech.py` and change the variables `path_to_wavlm_feat` and `tfrecord_path` accordingly. You need to change `path_to_wavlm_feat` to where the speech representations are stored in the previous step.
+6. Start Training by the following command: 
+   `python scripts/run.py --cfg_file=./exp/speech_XXL_cond/params.json --mode=train`
+   
+   If `tfrecord_path` doesn't exist, our codes will create tfrecords and save them to `tfrecord_path` before training starts. Note that if you encounter numerical issues ("NaN, INF") when the training starts, just try re-run the command multiple times. Training los will be saved to `./exp/speech_XXL_cond/`.
diff --git a/Phoneme_Hallucinator_v2/__pycache__/__init__.cpython-310.pyc b/Phoneme_Hallucinator_v2/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dd10e57fb796e93d3b29307545e818146e018c78
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diff --git a/Phoneme_Hallucinator_v2/datasets/__init__.py b/Phoneme_Hallucinator_v2/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b74c2b1b241eaa4e609448b8148405c040ea9223
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/datasets/__init__.py
@@ -0,0 +1,27 @@
+import os
+import pickle
+
+def get_dataset(args, split):
+    if args.dataset == 'speech':
+        from .speech import Dataset
+        dataset = Dataset(split, args.batch_size, args.set_size, args.mask_type)
+    else:
+        raise ValueError()
+
+    return dataset
+    
+def cache(args, split, fname):
+    if os.path.isfile(fname):
+        with open(fname, 'rb') as f:
+            batches = pickle.load(f)
+    else:
+        batches = []
+        dataset = get_dataset(args, split)
+        dataset.initialize()
+        for _ in range(dataset.num_batches):
+            batch = dataset.next_batch()
+            batches.append(batch)
+        with open(fname, 'wb') as f:
+            pickle.dump(batches, f)
+
+    return batches
diff --git a/Phoneme_Hallucinator_v2/datasets/__pycache__/__init__.cpython-36.pyc b/Phoneme_Hallucinator_v2/datasets/__pycache__/__init__.cpython-36.pyc
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index 0000000000000000000000000000000000000000..1030358bf57159d9c91f478f612dce7f96098401
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diff --git a/Phoneme_Hallucinator_v2/datasets/__pycache__/speech.cpython-36.pyc b/Phoneme_Hallucinator_v2/datasets/__pycache__/speech.cpython-36.pyc
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diff --git a/Phoneme_Hallucinator_v2/datasets/speech.py b/Phoneme_Hallucinator_v2/datasets/speech.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad7f54f1cd74864b0117aa8881379bd3df8d0393
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/datasets/speech.py
@@ -0,0 +1,278 @@
+import h5py
+import numpy as np
+from pathlib import Path
+import tensorflow as tf
+import torch
+from tqdm import tqdm
+import os
+import sys
+import glob
+import random
+import pdb
+np.random.seed(0)
+generate_tf_record = False
+
+tfrecord_path = "/path/to/save/your/tfrecord/"
+path_to_wavlm_feat = "/path/to/your/wavlm/feat"
+
+if not os.path.exists(tfrecord_path):
+    generate_tf_record = True
+os.makedirs(tfrecord_path, exist_ok=True)
+train_filename = tfrecord_path + 'train'
+valid_filename= tfrecord_path + 'valid'
+test_filename= tfrecord_path + 'test'
+train_path = Path(os.path.join(path_to_wavlm_feat, "train-clean-100"))
+valid_path = Path(os.path.join(path_to_wavlm_feat, "dev-clean"))
+test_path = Path(os.path.join(path_to_wavlm_feat, "test-clean"))
+
+train_size = 27269
+valid_size = 1940
+test_size = 1850
+
+def get_filenames(path):
+    all_files = []
+    all_files.extend(list(path.rglob("**/*.pt")))
+    return all_files
+
+def length_filter(paths):
+    filtered_paths = []
+    print("filter short files")
+    for each in tqdm(paths):
+        data = torch.load(each).numpy().astype(np.float32)
+        if data.shape[0] < 200:
+            continue
+        filtered_paths.append(each)
+    return filtered_paths
+
+
+def generate_mask(x, mask_type):
+    if mask_type == b'expand':
+        m = np.zeros_like(x)
+        N = np.random.randint(x.shape[0]//8, x.shape[0])
+        ind = np.random.choice(x.shape[0], N, replace=False)
+        m[ind] = 1.
+    elif mask_type == b'few_expand':
+        m = np.zeros_like(x)
+        N = np.random.randint(x.shape[0]//8)
+        ind = np.random.choice(x.shape[0], N, replace=False)
+        m[ind] = 1.
+    elif mask_type == b'arb_expand':
+        m = np.zeros_like(x)
+        N = np.random.randint(x.shape[0])
+        ind = np.random.choice(x.shape[0], N, replace=False)
+        m[ind] = 1.
+    elif mask_type == b'det_expand':
+        m = np.zeros_like(x)
+        ind = np.random.choice(x.shape[0], 100, replace=False)
+        m[ind] = 1.
+    elif mask_type == b'complete':
+        m = np.zeros_like(x)
+        while np.sum(m[:,0]) < x.shape[0] // 8:
+            p = np.random.uniform(-0.5, 0.5, size=4)
+            xa = np.concatenate([x, np.ones([x.shape[0],1])], axis=1)
+            m = (np.dot(xa, p) > 0).astype(np.float32)
+            m = np.repeat(np.expand_dims(m, axis=1), 3, axis=1)
+    else:
+        raise ValueError()
+
+    return m
+
+
+def wrap_int64(value):
+    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
+def wrap_bytes(value):
+    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
+def print_progress(count, total):
+    # Percentage completion.
+    pct_complete = float(count) / total
+
+    # Status-message.
+    # Note the \r which means the line should overwrite itself.
+    msg = "\r- Progress: {0:.1%}".format(pct_complete)
+
+    # Print it.
+    sys.stdout.write(msg)
+    sys.stdout.flush()
+def convert(image_paths, out_path, max_files=1000):
+    # Args:
+    # image_paths   List of file-paths for the images.
+    # labels        Class-labels for the images.
+    # out_path      File-path for the TFRecords output file.
+    
+    print("Converting: " + out_path)
+    
+    # Number of images. Used when printing the progress.
+    num_images = len(image_paths)
+    splits = (num_images//max_files) + 1 
+    if num_images%max_files == 0:
+        splits-=1
+    print(f"\nUsing {splits} shard(s) for {num_images} files, with up to {max_files} samples per shard")
+    file_count = 0
+    for i in tqdm(range(splits)):
+        # Open a TFRecordWriter for the output-file.
+        with tf.io.TFRecordWriter("{}_{}_{}.tfrecords".format(out_path, i+1, splits)) as writer:
+            
+            # Iterate over all the image-paths and class-labels.
+            current_shard_count = 0
+            while current_shard_count < max_files: 
+                index = i*max_files+current_shard_count
+                if index == len(image_paths):
+                    break
+                current_image = image_paths[index]
+
+                # Load the image-file using matplotlib's imread function.
+                img = torch.load(current_image).numpy().astype(np.float32)
+                
+                # Convert the image to raw bytes.
+                img_bytes = img.tostring()
+
+                # Create a dict with the data we want to save in the
+                # TFRecords file. You can add more relevant data here.
+                data = \
+                    {
+                        'image': wrap_bytes(img_bytes),
+                        'length': wrap_int64(img.shape[0]),
+                        "filename": wrap_bytes(bytes(os.path.splitext(current_image.name)[0], 'utf-8'))
+                    }
+
+                # Wrap the data as TensorFlow Features.
+                feature = tf.train.Features(feature=data)
+
+                # Wrap again as a TensorFlow Example.
+                example = tf.train.Example(features=feature)
+
+                # Serialize the data.
+                serialized = example.SerializeToString()
+                
+                # Write the serialized data to the TFRecords file.
+                writer.write(serialized)
+                current_shard_count+=1
+                file_count += 1
+    print(f"\nWrote {file_count} elements to TFRecord")
+
+
+if generate_tf_record:
+    train_image_paths = length_filter(get_filenames(train_path))
+    valid_image_paths = length_filter(get_filenames(valid_path))
+    test_image_paths = length_filter(get_filenames(test_path))
+    print(f"Number of training data after length filering: {len(train_image_paths)}")
+    print(f"Number of valid data after length filering: {len(valid_image_paths)}")
+    print(f"Number of testing data after length filering: {len(test_image_paths)}")
+    random.Random(4).shuffle(train_image_paths)
+
+    train_size = len(train_image_paths)
+    valid_size = len(valid_image_paths)
+    test_size = len(test_image_paths)
+    convert(image_paths=train_image_paths,
+            out_path=train_filename)
+
+    convert(image_paths=valid_image_paths,
+            out_path=valid_filename)
+
+    convert(image_paths=test_image_paths,
+            out_path=test_filename)
+    
+
+def parse(serialized):
+    # Define a dict with the data-names and types we expect to
+    # find in the TFRecords file.
+    # It is a bit awkward that this needs to be specified again,
+    # because it could have been written in the header of the
+    # TFRecords file instead.
+    features = \
+        {
+            'image': tf.io.FixedLenFeature([], tf.string),
+            'length': tf.io.FixedLenFeature([], tf.int64),
+            'filename': tf.io.FixedLenFeature([], tf.string),
+        }
+
+    # Parse the serialized data so we get a dict with our data.
+    parsed_example = tf.io.parse_single_example(serialized=serialized,
+                                             features=features)
+
+    # Get the image as raw bytes.
+    image_raw = parsed_example['image']
+
+    # Decode the raw bytes so it becomes a tensor with type.
+    image = tf.io.decode_raw(image_raw, tf.float32)
+    
+
+    # Get the label associated with the image.
+    length = parsed_example['length']
+
+    image = tf.reshape(image, [length, 1024])
+    filename = parsed_example['filename']
+
+    # The image and label are now correct TensorFlow types.
+    return image, filename
+
+def process(x, filename, set_size, mask_type):
+    x = x/10
+    ind = np.random.choice(x.shape[0], set_size, replace=False)
+    x = x[ind] 
+    m = generate_mask(x, mask_type)
+    #N = np.random.randint(set_size)
+    #S = np.random.randint(x.shape[0] - set_size + 1)
+    #x = x[S:S+set_size]
+    #m = np.zeros_like(x)
+    #S = np.random.randint(set_size - N + 1)
+    #m[S:S+N] = 1.0
+    return x, m, filename
+
+
+def get_dst(split, set_size, mask_type):
+    if split == 'train':
+        files = glob.glob(train_filename+"*.tfrecords", recursive=False)
+        dst = tf.data.TFRecordDataset(files)
+        size = train_size
+        dst = dst.map(parse)
+        dst = dst.shuffle(256)
+        dst = dst.map(lambda x, y: tuple(tf.compat.v1.py_func(process, [x, y, set_size, mask_type], [tf.float32, tf.float32, tf.string])), num_parallel_calls=8)
+    elif split == 'valid':
+        files = glob.glob(valid_filename+"*.tfrecords", recursive=False)
+        dst = tf.data.TFRecordDataset(files)
+        size = valid_size
+        dst = dst.map(parse)
+        dst = dst.map(lambda x, y: tuple(tf.compat.v1.py_func(process, [x, y, set_size, mask_type], [tf.float32, tf.float32, tf.string])), num_parallel_calls=8)
+    else:
+        files = glob.glob(test_filename+"*.tfrecords", recursive=False)
+        dst = tf.data.TFRecordDataset(files)
+        size = test_size
+        dst = dst.map(parse)
+        dst = dst.map(lambda x, y: tuple(tf.compat.v1.py_func(process, [x, y, set_size, mask_type], [tf.float32, tf.float32, tf.string])), num_parallel_calls=8)
+    return dst, size
+
+class Dataset(object):
+    def __init__(self, split, batch_size, set_size, mask_type):
+        g = tf.Graph()
+        with g.as_default():
+            # open a session
+            config = tf.compat.v1.ConfigProto()
+            config.log_device_placement = True
+            config.allow_soft_placement = True
+            config.gpu_options.allow_growth = True
+            self.sess = tf.compat.v1.Session(config=config, graph=g)
+            # build dataset
+            dst, size = get_dst(split, set_size, mask_type)
+            self.size = size
+            self.num_batches = self.size // batch_size
+            dst = dst.batch(batch_size, drop_remainder=False)
+            dst = dst.prefetch(1)
+
+            dst_it = tf.compat.v1.data.make_initializable_iterator(dst)
+            x, b, filename  = dst_it.get_next()
+            self.x = x
+            self.b = b
+            self.filename = filename
+            #self.x = tf.reshape(x, [batch_size, set_size, 1024])
+            #self.b = tf.reshape(b, [batch_size, set_size, 1024])
+            self.dimension = 1024
+            self.initializer = dst_it.initializer
+
+    def initialize(self):
+        self.sess.run(self.initializer)
+
+    def next_batch(self):
+        x, b, filename = self.sess.run([self.x, self.b, self.filename])
+        m = np.ones_like(b)
+        return {'x':x, 'b':b, 'm':m, "f":filename}
\ No newline at end of file
diff --git a/Phoneme_Hallucinator_v2/evaluation/ASR-Eval.ipynb b/Phoneme_Hallucinator_v2/evaluation/ASR-Eval.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f29303777b8019651e45392fa6ff734ecb55edea
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/evaluation/ASR-Eval.ipynb
@@ -0,0 +1,2928 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "79af4de2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "\n",
+    "try:\n",
+    "    import tensorflow  # required in Colab to avoid protobuf compatibility issues\n",
+    "except ImportError:\n",
+    "    pass\n",
+    "\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "import whisper\n",
+    "import torchaudio\n",
+    "import re\n",
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "\n",
+    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dabded15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SpeechData(torch.utils.data.Dataset):\n",
+    "    def __init__(self, device=DEVICE):\n",
+    "        self.device = device\n",
+    "        self.dataset = []\n",
+    "        self.data_path = \"/media/leo/OS/knn-vc-master/eval/expand_result_XXL_cond_unpeq_100k_nondenoise\"\n",
+    "        self.source_path = \"/media/leo/OS/knn-vc-master/eval/data\"\n",
+    "        for each in os.listdir(self.data_path):\n",
+    "            self.dataset.append(os.path.join(self.data_path, each))\n",
+    "        self.p = re.compile(\"{(.*)}_{(.*)}_to_.*.wav\")\n",
+    "    def __len__(self):\n",
+    "        return len(self.dataset)\n",
+    "    \n",
+    "    def __getitem__(self, item):\n",
+    "        path = self.dataset[item]\n",
+    "        text = self.wav_to_text(path)\n",
+    "        audio, sr = torchaudio.load(path)\n",
+    "        assert sr == 16000\n",
+    "        audio = whisper.pad_or_trim(audio.flatten()).to(self.device)\n",
+    "        mel = whisper.log_mel_spectrogram(audio)\n",
+    "        return (mel, text, audio)\n",
+    "    \n",
+    "    def wav_to_text(self, path):\n",
+    "        result = self.p.search(path)\n",
+    "        spk = result.group(1)\n",
+    "        utt = result.group(2)\n",
+    "        utt_split = utt.split(\"-\")\n",
+    "        text_file = utt_split[0] + \"-\" + utt_split[1] + \".trans.txt\"\n",
+    "        f = open(os.path.join(self.source_path, spk, text_file), 'r')\n",
+    "        text_annotation = f.readlines()\n",
+    "        for each in text_annotation:\n",
+    "            if utt in each:\n",
+    "                return each[len(utt)+1:].strip()\n",
+    "        \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "51b29135",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SpeechData(torch.utils.data.Dataset):\n",
+    "    def __init__(self, device=DEVICE):\n",
+    "        self.device = device\n",
+    "        self.dataset = []\n",
+    "        self.source_path = \"/media/leo/OS/knn-vc-master/eval/data\"\n",
+    "        for spk in os.listdir(self.source_path):\n",
+    "            for utt in os.listdir(os.path.join(self.source_path, spk)):\n",
+    "                if utt.endswith(\"txt\"):\n",
+    "                    continue\n",
+    "                self.dataset.append(os.path.join(self.source_path, spk, utt))\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.dataset)\n",
+    "    \n",
+    "    def __getitem__(self, item):\n",
+    "        path = self.dataset[item]\n",
+    "        text = self.wav_to_text(path)\n",
+    "        audio, sr = torchaudio.load(path)\n",
+    "        assert sr == 16000\n",
+    "        audio = whisper.pad_or_trim(audio.flatten()).to(self.device)\n",
+    "        mel = whisper.log_mel_spectrogram(audio)\n",
+    "        return (mel, text, audio)\n",
+    "    \n",
+    "    def wav_to_text(self, path):\n",
+    "        base_name = os.path.basename(path).split(\".\")[0]\n",
+    "        dir_name = os.path.dirname(path)\n",
+    "        text_name = base_name.split(\"-\")\n",
+    "        text_name = text_name[0] + \"-\" + text_name[1] + \".trans.txt\"\n",
+    "        f = open(os.path.join(dir_name, text_name), 'r')\n",
+    "        text_annotation = f.readlines()\n",
+    "        for each in text_annotation:\n",
+    "            if base_name in each:\n",
+    "                return each[len(base_name)+1:].strip()\n",
+    "        \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "6e9792fe",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model is English-only and has 71,825,408 parameters.\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = torch.utils.data.DataLoader(SpeechData(), batch_size=1)\n",
+    "model = whisper.load_model(\"base.en\")\n",
+    "print(\n",
+    "    f\"Model is {'multilingual' if model.is_multilingual else 'English-only'} \"\n",
+    "    f\"and has {sum(np.prod(p.shape) for p in model.parameters()):,} parameters.\"\n",
+    ")\n",
+    "options = whisper.DecodingOptions(language=\"en\", without_timestamps=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "45e1dc8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████████| 100/100 [00:09<00:00, 10.10it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "hypotheses = []\n",
+    "references = []\n",
+    "from tqdm import tqdm\n",
+    "for mels, texts, audio in tqdm(loader):\n",
+    "    results = model.decode(mels, options)\n",
+    "    hypotheses.extend([result.text for result in results])\n",
+    "    references.extend(texts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "028a4d4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies, that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies, that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract the watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hood, and Stutley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stullie had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stullie had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hood, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate would be passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we be passed by.',\n",
+       " 'Warrenton spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate would be passed by.',\n",
+       " 'Warrenton spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we we passed by.',\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we be passed by.',\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to thank Jeffrey's claims to the estate we passed by.\",\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate would be passed by.\",\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to thank Jeffrey's claims to the estate we we passed by.\",\n",
+       " 'Warrenton spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we we passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we we passed by.',\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and steadily, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, which hasten his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the ow-walls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game-wealth that this passageway led from the hut in a pleasant to Sherwood, and that Jeffrey for the time was hiding with the owaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game well that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasantest to Sherwood, and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game-well that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game-wall that this passageway led from the hut in the pleasantest to Sherwood, and that Jeffrey for the time was hiding with the ow-walls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game well that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game well that this passageway led from the hut in the Pleasance to Sherwood, and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasantest to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " 'But take it whilst I live, and wear a monthy shade shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.',\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stuchen I warrant me.\",\n",
+       " \"But take it will stay live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear a Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutchen I warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutchen I warrant me.\",\n",
+       " \"But take it will still live, and wear a monthy she's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.\",\n",
+       " \"But take it whilst I live, and wear a monthy she's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutche and I warn't me.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stethly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his own master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hood, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hood, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hood, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the HUD, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'Warrenton spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate where he passed by.',\n",
+       " \"Warrington spoke thus with significance to show Robin that he was not to thank Jeffries' claims to the estate we passed by.\",\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate we we passed by.\",\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate will be passed by.\",\n",
+       " \"Warrenton spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate we passed by.\",\n",
+       " \"Warrenton spoke thus with significance, to show Robin that he was not to thank Geoffrey's claims to the estate we passed by.\",\n",
+       " \"Warrenton spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate would be passed by.\",\n",
+       " \"Warrenton spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate we repast by.\",\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate were he passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we passed by.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasants to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasants to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasants to Sherwood and that Jeffrey for the time was hiding with the Alwalls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasants to Sherwood, and that Jeffrey for the time was hiding with the Alwals in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game well that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game wall that this passageway led from the hut in the Pleasants to Sherwood and that Jeffrey for the time was hiding with the Alwalls in the forest.',\n",
+       " \"But take it whilst I live, and wear a montfiche as shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, For you will never disgrace our stutch and I warn't me.\",\n",
+       " 'But take it whilst I live, and wear a monthy shades shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn me.',\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " 'But take it whilst I live, and wear a montfiche as sheald in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.',\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, For you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear a Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, For you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutche and I warn't me.\",\n",
+       " \"Will cried he softly, and stuttly, which was at his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, which was at his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stuntley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " \"Warrington spoke thus with significance to show Robin that he was not to think Geoffrey's claims to the estate we passed by.\",\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate were he passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we we passed by.',\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we we passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we we passed by.',\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate where he passed by.',\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate would be passed by.',\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we passed by.',\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to thank Jeffrey's claims to the estate we passed by.\",\n",
+       " 'The old servant told him quietly as they crept back to game well that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game wall that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the owl walls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game hall that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game well that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood, and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the owl walls in the forest.',\n",
+       " \"But take it willst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, For you will never disgrace our stutchen I warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But ticket will still live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.\",\n",
+       " 'But take it will style live, and wear monfiches shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.',\n",
+       " 'But take it whilst I live, and wear Montfiche\\'s shield in the days when my eyes can be rejoiced by so brave a sight, For you will never disgrace our stutchen I warn\\'t me.\"',\n",
+       " \"But take it willst I live, and wear a monthy shades shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"Will cried he softly, and stuttly, which was in his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, which was at his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, which was at his couch across the door of his own master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his cow to cross the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his own master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who it shows at his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stullia tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stuttley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'Warrenton spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we we passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we passed by.',\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate were he passed by.\",\n",
+       " 'Warrenton spoke thus with significance, to show Robin that he was not to think Jeffries claimed to the estate we passed by.',\n",
+       " \"Warrington spoke thus with significance to show Robin that he was not to think Geoffrey's claims to the estate we passed by.\",\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we passed by.',\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate where he passed by.',\n",
+       " \"Warrington spoke thus with significance to show Robin that he was not to think Jeffery's claims to the estate where he passed by.\",\n",
+       " 'Warrenton spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate would be passed by.',\n",
+       " 'The old servant told him quietly as the crept back to game while the this passageway led from the hunt in the Pleasants to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasantest to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasants to Sherwood and that Jeffrey for the time was hiding with the ow-walls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game well that this passageway led from the hut in the pleasant to Sherwood and the Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as the crepe backed a game well that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as the crepe backed a game while that this passageway led from the hut in the pleasant to Sherwood and the Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game well that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasantest disjure wood, and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " \"But take it whilst I live, and wear monthish as she old in the days when my eyes can be rejoiced by so brave a sight, For you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But ticket will styliv, and wear monfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " 'But take it will still live, and wear a monthy shades shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.',\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.\",\n",
+       " \"But take it whilst I live, and wear a monthy shades shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " 'But take it whilst I live, and wear a monthish haze shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stuttley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stutley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we be passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we we passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we be passed by.',\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we passed by.',\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, which was at his couch across the door of his own master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his own master's chamber sprang up at once at answer.\",\n",
+       " \"Will, cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will, cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stutley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stutley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the HUD, and Stuttley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'The old servant told him quietly as they crept back to game-well that this passageway led from the hut in the pleasantest to Sherwood, and that Jeffrey for the time was hiding with the ow-walls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game-wall that this passageway led from the hut in the pleasant to Sherwood, and that Jeffrey for the time was hiding with the owl-walls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game-well that this passageway led from the hut in the pleasantest to Sherwood, and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as the crepe backed a game-wolve that this passageway led from the hut in the pleasantest to Sherwood, and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the ow-walls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasantest to Sherwood, and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasantest to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while this passageway led from the hut in the pleasant to Sherwood, and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we've passed by.\",\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we we passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claimed to the estate we we passed by.',\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate where he passed by.',\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to thank Jeffrey's claims to the estate we we passed by.\",\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate would be passed by.',\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate we we passed by.\",\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we we passed by.',\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate we we passed by.\",\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to thank Jeffries claims to the estate we passed by.',\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutchen I warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take you willst die live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.\",\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutchen I warrant me.\",\n",
+       " \"But take it whilst I live, and wear a Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hood, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stullie had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stuttley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber, sprang up at once in answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, which chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will, cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, which hast chosen his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we passed by.',\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we passed by.',\n",
+       " 'Warrenton spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we we passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate where he passed by.',\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate would be passed by.\",\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to thank Geoffrey's claims to the estate where he passed by.\",\n",
+       " \"Warrenton spoke thus with significance to show Robin that he was not to thank Geoffrey's claims to the estate we passed by.\",\n",
+       " \"Warrington spoke thus with significance to show Robin that he was not to think Geoffrey's claims to the estate we\",\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate we've passed by.\",\n",
+       " \"Warrenton spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate we passed by.\",\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasantest to Sherwood, and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasantest to Sherwood, and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood, and that Jeffrey for the time was hiding with the owalls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game-well that this passageway led from the hut in the pleasant to Sherwood, and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasant to Sherwood, and that Jeffrey for the time was hiding with the owalls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood, and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while this passageway led from the hut in the Pleasants to Sherwood, and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear Montviché's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutchen I warrant me.\",\n",
+       " \"But take it whilst I live, and wear a monthy shades shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear a monthy she's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.\",\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " 'But take it whilst I live, and wear monfiches shield in the days when my eyes can be rejoiced by so brave a sight, for ye will never disgrace our stutch and I warrant me.',\n",
+       " \"But take it whilst I live, and wear a monthy shades shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.\",\n",
+       " 'But take it whilst I live, and wear a monthy shades shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.',\n",
+       " \"Will cried he softly, and Stuttley, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, which hast chosen his couch across the door of his young master's chamber, sprang up at once in answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stutley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stutley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stutley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the HUD, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stutley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we read past by.',\n",
+       " \"Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we've passed by.\",\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we we passed by.',\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we passed by.',\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate would be passed by.\",\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we've passed by.\",\n",
+       " \"Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we've passed by.\",\n",
+       " \"Warrenton spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we've passed by.\",\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate where he passed by.',\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we've passed by.\",\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood, and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the owaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the owaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while this passageway led from the hut in the pleasant to Sherwood and the Jeffery for the time was hiding with the Al laws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the owl walls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the outwalls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " 'But take it whilst I live, and wear monthish as she old in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.',\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " 'But take it whilst I live, and wear a monthy shades shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.',\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutchen I warrant me.\",\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutchen I warrant me.\",\n",
+       " \"But take it whilst I live, and wear monfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it willst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutchen I warn't me.\",\n",
+       " 'But take it willst I live, and wear a monthy shades shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.',\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " 'But take it whilst I live, and wear monfiche as shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.',\n",
+       " \"But take it whilst I live, and wear a monthy she's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutchen I warrant me.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his own master's chamber, sprang up at once in answer.\",\n",
+       " \"Will cried he softly, and stuttly, which hasten his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once in answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber sprang up at once at answer.\",\n",
+       " \"Will, cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, which was in his couch across the door of his own master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, which was at his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber sprang up at once an answer.\",\n",
+       " \"Will cried he softly. And stuttly, which was in his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stuttley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hood, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stuttley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stuntley had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " \"Warrenton spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate we passed by.\",\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate who he passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we be passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate where he passed by.',\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we passed by.',\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we passed by.',\n",
+       " \"Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we've passed by.\",\n",
+       " \"Warrington spoke thus with significance to show Robin that he was not to thank Jeffries' claims to the estate we passed by.\",\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we be passed by.',\n",
+       " \"Warrington spoke thus with significance to show Robin that he was not to thank Jeffries' claims to the estate where he passed by.\",\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we be passed by.',\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we passed by.',\n",
+       " 'Warrenton spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we we passed by.',\n",
+       " \"Warrenton spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we've passed by.\",\n",
+       " 'Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we we passed by.',\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to thank Jeffrey's claims to the estate we passed by.\",\n",
+       " \"Warrenton spoke thus with significance to show Robin that he was not to think Geoffrey's claims to the estate we passed by.\",\n",
+       " 'Warrington spoke thus with significance to show Robin that he was not to think Jeffries claims to the estate we be passed by.',\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to think Geoffrey's claims to the estate will be passed by.\",\n",
+       " \"Warrington spoke thus with significance, to show Robin that he was not to think Jeffries claims to the estate we've passed by.\",\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood, and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the Alwals in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the pleasantest to Sherwood, and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the owls in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hunt and the peasants to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasants to Sherwood and that Jeffrey for the time was hiding with the Alwals in the forest.',\n",
+       " 'The old servant told him quietly as the crepe backed a game while that this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as the crepe backed a game wall that this passageway led from the hut in the Pleasance to Sherwood, and that Jeffrey for the time was hiding with the owaws in the forest.',\n",
+       " 'The old servant told him quietly as the crepe backed a game while that this passageway led from the hut in the Pleasance to Sherwood, and that Jeffrey for the time was hiding with the Alwats in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while this passageway led from the hut in the pleasant to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game while that this passageway led from the hut in the Pleasance to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game well that this passageway led from the hut in the pleasantest to Sherwood and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " 'The old servant told him quietly as they crept back to game hall that this passageway led from the hut in the pleasantest to Sherwood, and that Jeffrey for the time was hiding with the outlaws in the forest.',\n",
+       " \"But take it whilst I live, and wear Montviché's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.\",\n",
+       " \"But take it whilst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " 'But take it whilst I live, and wear a monthy shades shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and eye warrant me.',\n",
+       " \"But take it, willst I live, and wear a monthy shades shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warn't me.\",\n",
+       " \"But take it willst I live, and wear Montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutchen I warn't me.\",\n",
+       " \"But take it whilst I live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutchen I warrant me.\",\n",
+       " \"But ticket will still live, and wear a montfiche's shield in the days when my eyes can be rejoiced by so brave a sight, for you will never disgrace our stutch and I warrant me.\",\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Com, Com returned Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Komm Komm returned Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now let us to business.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies, that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his own master's chamber, sprang up at once at answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his kout to cross the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " \"Will cried he softly, and stuttly, who had chosen his couch across the door of his young master's chamber, sprang up at once an answer.\",\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stully had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'They were upon the verge of an open trap in the far corner of the hut, and Stulli had tripped over the edge of the reversed flat mouth of this pit.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little net hold that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little Nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself as one without a cross of blood, though it is not always easy to deal with an Indian as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself as one without a cross of blood, though it is not always easy to deal with an Indian as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then heaving a heavy sigh. Probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tins of red to it that the winds of the heaven and the sun have not bestowed. Now let us to business.',\n",
+       " 'Come, come, returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tins of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Com, Com returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now let us to business.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little Nell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh. Probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract the watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself as one without a cross of blood, though it is not always easy to deal with an Indian as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then heaving a heavy sigh. Probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'The only duplicate which existed so far as I knew was that which belonged to my servant, Banister, a man who was looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " 'I was hoping that if the paper on which he wrote was thin, some trace of it might come through upon this polished surface. Now I see nothing.',\n",
+       " 'He stood a moment bewildered, then turned and rushed upon the island. A great sheet of dazzling sunlight swept the place, and beneath lay a mighty mass of olive green. Thick, tall, wet and willowy.',\n",
+       " 'She rolls with a fleeting glance, gathered the shawl around her, then gliding forward, wavering tremulous, slipped across the road and into the swamp.',\n",
+       " 'On she hurried until sweeping down to the lagoon and the island low the cotton lay before her.',\n",
+       " \"But when it came to be a question of lighting a scattered suburb, a group of dwellings on the outskirts, a remote country residence, or a farmhouse, the alternating current in all elements save its danger was Andy's ideal.\",\n",
+       " 'Come, come, return, Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little Nell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Andean, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'Come, come, returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the waver in confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return, Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the waver in confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the waver in confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the waver in confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, and casing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little Nell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, returned David a little Nell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself as one without a cross of blood, though it is not always easy to deal with an Indian as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself as one without a cross of blood, though it is not always easy to deal with an Indian as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then heaving a heavy sigh. Probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then heaving a heavy sigh. Probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies, that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " 'Come, come, returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, and casing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds or the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Com, Com returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " 'Come, come, return, Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew and pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed, now let us to business.',\n",
+       " 'Come, come, returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, uncasing his honest countenance, the better to assure the waver in confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little n e l that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little n e l that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " \"Come, come, return, Hawkeye, I'm casing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.\",\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Komm Komm returned Hawkeye and casing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Com-com returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Komm Komm returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, returned David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little net hold it so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nel that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, is greatly given to mercy and love, return David a little Nell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " 'In his return to the camp, his acute and practiced intellects were intently engaged in devising means to counteract a watchfulness and suspicion on the part of his enemies that he knew were in no degree inferior to his own.',\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of a bear's singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " \"In other words, while he had implicit faith in the ability of Balaam's ass to speak, he was somewhat skeptical on the subject of the bears singing, and yet he had been assured of the latter on the testimony of his own exquisite organs.\",\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the waver in confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white, his one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, uncasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the waver in confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, uncasing his honest countenance, the better to assure the waver in confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, returned Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'Come, come, return Hawkeye, encasing his honest countenance, the better to assure the wavering confidence of his companion. You may see a skin which, if it be not as white as one of the gentle ones, has no tinge of red to it that the winds of the heaven and the sun have not bestowed. Now, let us to business.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little n e l that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper, I humbly trust, is greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust, as greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little Nell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little nettle that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'My pursuits are peaceful, and my temper I humbly trust is greatly given to mercy and love, return David a little knell that so direct an attack on his manhood. But there are none who can say that I have ever forgotten my faith in the Lord, even in the greatest straits.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself as one without a cross of blood, though it is not always easy to deal with an Indian as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself as one without a cross of blood, though it is not always easy to deal with an Indian as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself as one without a cross of blood, though it is not always easy to deal with an Indian as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh. Probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, it is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew in pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'Then, heaving a heavy sigh, probably among the last he ever drew, and pining for a condition he had so long abandoned, he added, It is what I would wish to practice myself, as one without a cross of blood, though it is not always easy to deal with an Indian, as you would with a fellow Christian.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place, or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'I will endeavor in my statement to avoid such terms as would serve to limit the events to any particular place or give a clue as to the people concerned.',\n",
+       " 'He was in the midst of that when your return caused him to make a very hurried retreat. Very hurried, since he had not time to replace the papers which would tell you that he had been there.',\n",
+       " 'He was in the midst of that when your return caused him to make a very hurried retreat. Very hurried, since he had not time to replace the papers which would tell you that he had been there.',\n",
+       " 'He was in the midst of that when your return caused him to make a very hurried retreat. Very hurried, since he had not time to replace the papers which would tell you that he had been there.',\n",
+       " 'He was in the midst of that when your return caused him to make a very hurried retreat. Very hurried, since he had not time to replace the papers which would tell you that he had been there.',\n",
+       " 'He was in the midst of that when your return caused him to make a very hurried retreat. Very hurried, since he had not time to replace the papers which would tell you that he had been there.',\n",
+       " 'He was in the midst of that when a return caused him to make a very hurried retreat. Very hurried, since he had not time to replace the papers which would tell you that he had been there.',\n",
+       " 'He was in the midst of that when your return caused him to make a very hurried retreat. Very hurried, since he had not time to replace the papers which would tell you that he had been there.',\n",
+       " 'He was in the midst of that when your return caused him to make a very hurried retreat. Very hurried, since he had not time to replace the papers which would tell you that he had been there.',\n",
+       " 'He was in the midst of that when your return caused him to make a very hurried retreat. Very hurried, since he had not time to replace the papers which would tell you that he had been there.',\n",
+       " 'The only duplicate which existed so far as I knew was that which belonged to my servant, Banister, a man who has looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " 'The only duplicate which existed so far as I knew was that which belonged to my servant, Banister, a man who has looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " 'The only duplicate which existed so far as I knew was that which belonged to my servant, Banister, a man who has looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " 'The only duplicate which existed so far as I knew was that which belonged to my servant, Banister, a man who was looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " 'The only duplicate which existed so far as I knew was that which belonged to my servant, Banister, a man who has looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " 'The only duplicate which existed so far as I knew was that which belonged to my servant, Banister, a man who has looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " 'The only duplicate which existed so far as I knew was that which belonged to my servant, Banister, a man who has looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " 'The only duplicate which existed so far as I knew was that which belonged to my servant, Banister, a man who has looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " 'The only duplicate which existed so far as I knew was that which belonged to my servant, Banister, a man who has looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'I had always known him to be restless in his manner, but on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.',\n",
+       " 'The only duplicate which existed so far as I knew was that which belonged to my servant, Banister, a man who has looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " 'The only duplicate which existed so far as I knew it was that which belonged to my servant, Banister, a man who has looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " 'The only duplicate which existed so far as I knew was that which belonged to my servant, Banister, a man who has looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " 'The only duplicate which existed so far as I knew was that which belonged to my servant, Banister, a man who has looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " 'The only duplicate which existed so far as I knew was that which belonged to my servant, Banister, a man who has looked after my room for ten years and whose honesty is absolutely above suspicion.',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hypotheses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dbe983b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " \"WARRENTON SPOKE THUS WITH SIGNIFICANCE TO SHOW ROBIN THAT HE WAS NOT TO THINK GEOFFREY'S CLAIMS TO THE ESTATE WOULD BE PASSED BY\",\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " 'THE OLD SERVANT TOLD HIM QUIETLY AS THEY CREPT BACK TO GAMEWELL THAT THIS PASSAGE WAY LED FROM THE HUT IN THE PLEASANCE TO SHERWOOD AND THAT GEOFFREY FOR THE TIME WAS HIDING WITH THE OUTLAWS IN THE FOREST',\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " \"BUT TAKE IT WHILST I LIVE AND WEAR MONTFICHET'S SHIELD IN THE DAYS WHEN MY EYES CAN BE REJOICED BY SO BRAVE A SIGHT FOR YOU WILL NE'ER DISGRACE OUR SCUTCHEON I WARRANT ME\",\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " \"WILL CRIED HE SOFTLY AND STUTELEY WHO HAD CHOSEN HIS COUCH ACROSS THE DOOR OF HIS YOUNG MASTER'S CHAMBER SPRANG UP AT ONCE IN ANSWER\",\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'THEY WERE UPON THE VERGE OF AN OPEN TRAP IN THE FAR CORNER OF THE HUT AND STUTELEY HAD TRIPPED OVER THE EDGE OF THE REVERSED FLAP MOUTH OF THIS PIT',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " 'I WAS HOPING THAT IF THE PAPER ON WHICH HE WROTE WAS THIN SOME TRACE OF IT MIGHT COME THROUGH UPON THIS POLISHED SURFACE NO I SEE NOTHING',\n",
+       " 'HE STOOD A MOMENT BEWILDERED THEN TURNED AND RUSHED UPON THE ISLAND A GREAT SHEET OF DAZZLING SUNLIGHT SWEPT THE PLACE AND BENEATH LAY A MIGHTY MASS OF OLIVE GREEN THICK TALL WET AND WILLOWY',\n",
+       " 'SHE ROSE WITH A FLEETING GLANCE GATHERED THE SHAWL ROUND HER THEN GLIDING FORWARD WAVERING TREMULOUS SLIPPED ACROSS THE ROAD AND INTO THE SWAMP',\n",
+       " 'ON SHE HURRIED UNTIL SWEEPING DOWN TO THE LAGOON AND THE ISLAND LO THE COTTON LAY BEFORE HER',\n",
+       " 'BUT WHEN IT CAME TO BE A QUESTION OF LIGHTING A SCATTERED SUBURB A GROUP OF DWELLINGS ON THE OUTSKIRTS A REMOTE COUNTRY RESIDENCE OR A FARM HOUSE THE ALTERNATING CURRENT IN ALL ELEMENTS SAVE ITS DANGER WAS AND IS IDEAL',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " 'IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTISED INTELLECTS WERE INTENTLY ENGAGED IN DEVISING MEANS TO COUNTERACT A WATCHFULNESS AND SUSPICION ON THE PART OF HIS ENEMIES THAT HE KNEW WERE IN NO DEGREE INFERIOR TO HIS OWN',\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " \"IN OTHER WORDS WHILE HE HAD IMPLICIT FAITH IN THE ABILITY OF BALAAM'S ASS TO SPEAK HE WAS SOMEWHAT SKEPTICAL ON THE SUBJECT OF A BEAR'S SINGING AND YET HE HAD BEEN ASSURED OF THE LATTER ON THE TESTIMONY OF HIS OWN EXQUISITE ORGANS\",\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'COME COME RETURNED HAWKEYE UNCASING HIS HONEST COUNTENANCE THE BETTER TO ASSURE THE WAVERING CONFIDENCE OF HIS COMPANION YOU MAY SEE A SKIN WHICH IF IT BE NOT AS WHITE AS ONE OF THE GENTLE ONES HAS NO TINGE OF RED TO IT THAT THE WINDS OF THE HEAVEN AND THE SUN HAVE NOT BESTOWED NOW LET US TO BUSINESS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'MY PURSUITS ARE PEACEFUL AND MY TEMPER I HUMBLY TRUST IS GREATLY GIVEN TO MERCY AND LOVE RETURNED DAVID A LITTLE NETTLED AT SO DIRECT AN ATTACK ON HIS MANHOOD BUT THERE ARE NONE WHO CAN SAY THAT I HAVE EVER FORGOTTEN MY FAITH IN THE LORD EVEN IN THE GREATEST STRAITS',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'THEN HEAVING A HEAVY SIGH PROBABLY AMONG THE LAST HE EVER DREW IN PINING FOR A CONDITION HE HAD SO LONG ABANDONED HE ADDED IT IS WHAT I WOULD WISH TO PRACTISE MYSELF AS ONE WITHOUT A CROSS OF BLOOD THOUGH IT IS NOT ALWAYS EASY TO DEAL WITH AN INDIAN AS YOU WOULD WITH A FELLOW CHRISTIAN',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'I WILL ENDEAVOUR IN MY STATEMENT TO AVOID SUCH TERMS AS WOULD SERVE TO LIMIT THE EVENTS TO ANY PARTICULAR PLACE OR GIVE A CLUE AS TO THE PEOPLE CONCERNED',\n",
+       " 'HE WAS IN THE MIDST OF THAT WHEN YOUR RETURN CAUSED HIM TO MAKE A VERY HURRIED RETREAT VERY HURRIED SINCE HE HAD NOT TIME TO REPLACE THE PAPERS WHICH WOULD TELL YOU THAT HE HAD BEEN THERE',\n",
+       " 'HE WAS IN THE MIDST OF THAT WHEN YOUR RETURN CAUSED HIM TO MAKE A VERY HURRIED RETREAT VERY HURRIED SINCE HE HAD NOT TIME TO REPLACE THE PAPERS WHICH WOULD TELL YOU THAT HE HAD BEEN THERE',\n",
+       " 'HE WAS IN THE MIDST OF THAT WHEN YOUR RETURN CAUSED HIM TO MAKE A VERY HURRIED RETREAT VERY HURRIED SINCE HE HAD NOT TIME TO REPLACE THE PAPERS WHICH WOULD TELL YOU THAT HE HAD BEEN THERE',\n",
+       " 'HE WAS IN THE MIDST OF THAT WHEN YOUR RETURN CAUSED HIM TO MAKE A VERY HURRIED RETREAT VERY HURRIED SINCE HE HAD NOT TIME TO REPLACE THE PAPERS WHICH WOULD TELL YOU THAT HE HAD BEEN THERE',\n",
+       " 'HE WAS IN THE MIDST OF THAT WHEN YOUR RETURN CAUSED HIM TO MAKE A VERY HURRIED RETREAT VERY HURRIED SINCE HE HAD NOT TIME TO REPLACE THE PAPERS WHICH WOULD TELL YOU THAT HE HAD BEEN THERE',\n",
+       " 'HE WAS IN THE MIDST OF THAT WHEN YOUR RETURN CAUSED HIM TO MAKE A VERY HURRIED RETREAT VERY HURRIED SINCE HE HAD NOT TIME TO REPLACE THE PAPERS WHICH WOULD TELL YOU THAT HE HAD BEEN THERE',\n",
+       " 'HE WAS IN THE MIDST OF THAT WHEN YOUR RETURN CAUSED HIM TO MAKE A VERY HURRIED RETREAT VERY HURRIED SINCE HE HAD NOT TIME TO REPLACE THE PAPERS WHICH WOULD TELL YOU THAT HE HAD BEEN THERE',\n",
+       " 'HE WAS IN THE MIDST OF THAT WHEN YOUR RETURN CAUSED HIM TO MAKE A VERY HURRIED RETREAT VERY HURRIED SINCE HE HAD NOT TIME TO REPLACE THE PAPERS WHICH WOULD TELL YOU THAT HE HAD BEEN THERE',\n",
+       " 'HE WAS IN THE MIDST OF THAT WHEN YOUR RETURN CAUSED HIM TO MAKE A VERY HURRIED RETREAT VERY HURRIED SINCE HE HAD NOT TIME TO REPLACE THE PAPERS WHICH WOULD TELL YOU THAT HE HAD BEEN THERE',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'I HAD ALWAYS KNOWN HIM TO BE RESTLESS IN HIS MANNER BUT ON THIS PARTICULAR OCCASION HE WAS IN SUCH A STATE OF UNCONTROLLABLE AGITATION THAT IT WAS CLEAR SOMETHING VERY UNUSUAL HAD OCCURRED',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " 'THE ONLY DUPLICATE WHICH EXISTED SO FAR AS I KNEW WAS THAT WHICH BELONGED TO MY SERVANT BANNISTER A MAN WHO HAS LOOKED AFTER MY ROOM FOR TEN YEARS AND WHOSE HONESTY IS ABSOLUTELY ABOVE SUSPICION',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "references"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "763f73fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
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+       "        vertical-align: top;\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hypothesis</th>\n",
+       "      <th>reference</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>In his return to the camp, his acute and pract...</td>\n",
+       "      <td>IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>In his return to the camp, his acute and pract...</td>\n",
+       "      <td>IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>In his return to the camp, his acute and pract...</td>\n",
+       "      <td>IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>In his return to the camp, his acute and pract...</td>\n",
+       "      <td>IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>In his return to the camp, his acute and pract...</td>\n",
+       "      <td>IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9495</th>\n",
+       "      <td>But first I will tell you that for many years ...</td>\n",
+       "      <td>BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9496</th>\n",
+       "      <td>But first I will tell you that for many years ...</td>\n",
+       "      <td>BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9497</th>\n",
+       "      <td>But first I will tell you that for many years ...</td>\n",
+       "      <td>BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9498</th>\n",
+       "      <td>But first I will tell you that for many years ...</td>\n",
+       "      <td>BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9499</th>\n",
+       "      <td>But first I will tell you that for many years ...</td>\n",
+       "      <td>BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9500 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             hypothesis  \\\n",
+       "0     In his return to the camp, his acute and pract...   \n",
+       "1     In his return to the camp, his acute and pract...   \n",
+       "2     In his return to the camp, his acute and pract...   \n",
+       "3     In his return to the camp, his acute and pract...   \n",
+       "4     In his return to the camp, his acute and pract...   \n",
+       "...                                                 ...   \n",
+       "9495  But first I will tell you that for many years ...   \n",
+       "9496  But first I will tell you that for many years ...   \n",
+       "9497  But first I will tell you that for many years ...   \n",
+       "9498  But first I will tell you that for many years ...   \n",
+       "9499  But first I will tell you that for many years ...   \n",
+       "\n",
+       "                                              reference  \n",
+       "0     IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...  \n",
+       "1     IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...  \n",
+       "2     IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...  \n",
+       "3     IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...  \n",
+       "4     IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...  \n",
+       "...                                                 ...  \n",
+       "9495  BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...  \n",
+       "9496  BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...  \n",
+       "9497  BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...  \n",
+       "9498  BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...  \n",
+       "9499  BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...  \n",
+       "\n",
+       "[9500 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.DataFrame(dict(hypothesis=hypotheses, reference=references))\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c65328f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hypothesis</th>\n",
+       "      <th>reference</th>\n",
+       "      <th>hypothesis_clean</th>\n",
+       "      <th>reference_clean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>In his return to the camp, his acute and pract...</td>\n",
+       "      <td>IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...</td>\n",
+       "      <td>in his return to the camp his acute and practi...</td>\n",
+       "      <td>in his return to the camp his acute and practi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>In his return to the camp, his acute and pract...</td>\n",
+       "      <td>IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...</td>\n",
+       "      <td>in his return to the camp his acute and practi...</td>\n",
+       "      <td>in his return to the camp his acute and practi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>In his return to the camp, his acute and pract...</td>\n",
+       "      <td>IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...</td>\n",
+       "      <td>in his return to the camp his acute and practi...</td>\n",
+       "      <td>in his return to the camp his acute and practi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>In his return to the camp, his acute and pract...</td>\n",
+       "      <td>IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...</td>\n",
+       "      <td>in his return to the camp his acute and practi...</td>\n",
+       "      <td>in his return to the camp his acute and practi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>In his return to the camp, his acute and pract...</td>\n",
+       "      <td>IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...</td>\n",
+       "      <td>in his return to the camp his acute and practi...</td>\n",
+       "      <td>in his return to the camp his acute and practi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9495</th>\n",
+       "      <td>But first I will tell you that for many years ...</td>\n",
+       "      <td>BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...</td>\n",
+       "      <td>but 1st i will tell you that for many years i ...</td>\n",
+       "      <td>but 1st i will tell you that for many years i ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9496</th>\n",
+       "      <td>But first I will tell you that for many years ...</td>\n",
+       "      <td>BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...</td>\n",
+       "      <td>but 1st i will tell you that for many years i ...</td>\n",
+       "      <td>but 1st i will tell you that for many years i ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9497</th>\n",
+       "      <td>But first I will tell you that for many years ...</td>\n",
+       "      <td>BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...</td>\n",
+       "      <td>but 1st i will tell you that for many years i ...</td>\n",
+       "      <td>but 1st i will tell you that for many years i ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9498</th>\n",
+       "      <td>But first I will tell you that for many years ...</td>\n",
+       "      <td>BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...</td>\n",
+       "      <td>but 1st i will tell you that for many years i ...</td>\n",
+       "      <td>but 1st i will tell you that for many years i ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9499</th>\n",
+       "      <td>But first I will tell you that for many years ...</td>\n",
+       "      <td>BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...</td>\n",
+       "      <td>but 1st i will tell you that for many years i ...</td>\n",
+       "      <td>but 1st i will tell you that for many years i ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9500 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             hypothesis  \\\n",
+       "0     In his return to the camp, his acute and pract...   \n",
+       "1     In his return to the camp, his acute and pract...   \n",
+       "2     In his return to the camp, his acute and pract...   \n",
+       "3     In his return to the camp, his acute and pract...   \n",
+       "4     In his return to the camp, his acute and pract...   \n",
+       "...                                                 ...   \n",
+       "9495  But first I will tell you that for many years ...   \n",
+       "9496  But first I will tell you that for many years ...   \n",
+       "9497  But first I will tell you that for many years ...   \n",
+       "9498  But first I will tell you that for many years ...   \n",
+       "9499  But first I will tell you that for many years ...   \n",
+       "\n",
+       "                                              reference  \\\n",
+       "0     IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...   \n",
+       "1     IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...   \n",
+       "2     IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...   \n",
+       "3     IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...   \n",
+       "4     IN HIS RETURN TO THE CAMP HIS ACUTE AND PRACTI...   \n",
+       "...                                                 ...   \n",
+       "9495  BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...   \n",
+       "9496  BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...   \n",
+       "9497  BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...   \n",
+       "9498  BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...   \n",
+       "9499  BUT FIRST I WILL TELL YOU THAT FOR MANY YEARS ...   \n",
+       "\n",
+       "                                       hypothesis_clean  \\\n",
+       "0     in his return to the camp his acute and practi...   \n",
+       "1     in his return to the camp his acute and practi...   \n",
+       "2     in his return to the camp his acute and practi...   \n",
+       "3     in his return to the camp his acute and practi...   \n",
+       "4     in his return to the camp his acute and practi...   \n",
+       "...                                                 ...   \n",
+       "9495  but 1st i will tell you that for many years i ...   \n",
+       "9496  but 1st i will tell you that for many years i ...   \n",
+       "9497  but 1st i will tell you that for many years i ...   \n",
+       "9498  but 1st i will tell you that for many years i ...   \n",
+       "9499  but 1st i will tell you that for many years i ...   \n",
+       "\n",
+       "                                        reference_clean  \n",
+       "0     in his return to the camp his acute and practi...  \n",
+       "1     in his return to the camp his acute and practi...  \n",
+       "2     in his return to the camp his acute and practi...  \n",
+       "3     in his return to the camp his acute and practi...  \n",
+       "4     in his return to the camp his acute and practi...  \n",
+       "...                                                 ...  \n",
+       "9495  but 1st i will tell you that for many years i ...  \n",
+       "9496  but 1st i will tell you that for many years i ...  \n",
+       "9497  but 1st i will tell you that for many years i ...  \n",
+       "9498  but 1st i will tell you that for many years i ...  \n",
+       "9499  but 1st i will tell you that for many years i ...  \n",
+       "\n",
+       "[9500 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import jiwer\n",
+    "from whisper.normalizers import EnglishTextNormalizer\n",
+    "\n",
+    "normalizer = EnglishTextNormalizer()\n",
+    "data[\"hypothesis_clean\"] = [normalizer(text) for text in data[\"hypothesis\"]]\n",
+    "data[\"reference_clean\"] = [normalizer(text) for text in data[\"reference\"]]\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5ee08c3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WER: 5.17 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "wer = jiwer.wer(list(data[\"reference_clean\"]), list(data[\"hypothesis_clean\"]))\n",
+    "print(f\"WER: {wer * 100:.2f} %\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "69d07cfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WER: 5.39 +- 6.47%\n"
+     ]
+    }
+   ],
+   "source": [
+    "wers = []\n",
+    "for i in range(len(list(data[\"reference_clean\"]))):\n",
+    "    wer = jiwer.wer(list(data[\"reference_clean\"])[i], list(data[\"hypothesis_clean\"])[i])\n",
+    "    wers.append(wer)\n",
+    "print(f\"WER: {np.mean(wers) * 100:.2f} +- {np.std(wers) * 100:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "545a1d85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 72.30it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([ 0.9981, -0.9844,  0.9353,  0.9083,  0.9383,  0.9480,  0.9983,  0.9970,\n",
+      "         0.9981,  0.9997,  0.9964,  0.9978,  0.9990,  0.9635,  0.9934,  0.9975,\n",
+      "         0.8338,  0.9894,  0.9578, -0.9975,  0.9953,  0.9596,  0.9953,  0.9887,\n",
+      "         0.9954,  0.9746,  0.9962, -0.9790,  0.8314,  0.9283,  0.9997,  0.9971,\n",
+      "         0.9963,  0.6858,  0.9914,  0.9941,  0.9965,  0.9992, -0.9959,  0.9965,\n",
+      "         0.9994,  0.9944,  0.9959,  0.9996,  0.9948,  0.9970,  0.9976,  0.9999,\n",
+      "         0.9999,  0.9850,  0.9986,  0.9825,  0.9440, -0.0214,  0.9859, -0.9651,\n",
+      "         0.9589,  0.9983,  0.9990,  0.9460,  0.9738,  0.9969,  0.9973,  0.3082,\n",
+      "         0.9769,  0.9979,  0.7766,  0.9944,  0.9991,  0.9967,  0.9934,  0.9946,\n",
+      "        -0.9440,  0.9767,  0.9771,  0.9917,  0.9971,  0.9858,  0.9900,  0.9984,\n",
+      "         0.9978,  0.9979,  0.9980,  0.9986,  0.9974,  0.9953,  0.9976,  0.9990,\n",
+      "        -0.9984,  0.9936,  0.9983,  0.9972,  0.9645,  0.9972,  0.9983, -0.9882,\n",
+      "         0.9991,  0.9962,  0.9985,  0.9994, -0.9706,  0.9977,  0.9863, -0.1965,\n",
+      "         0.9985, -0.9956,  0.9946,  0.9948,  0.9938,  0.8593,  0.9988,  0.9999,\n",
+      "         0.9963, -0.1762,  0.0787,  0.9997,  0.9964,  0.9922,  0.9989, -0.9921,\n",
+      "         0.9869,  0.9956,  0.7127,  0.9973,  0.9936,  0.9966,  0.9995,  0.9888,\n",
+      "         0.9641,  0.9744,  0.9979,  0.9333,  0.9849, -0.7105, -0.0874,  0.9975,\n",
+      "         0.9906, -0.9769,  0.9971, -0.9742,  0.9845,  0.9947, -0.9498,  0.9937,\n",
+      "         0.9999,  0.4203, -0.7757, -0.5552,  0.9942,  0.9936,  0.9713,  0.9356,\n",
+      "         0.9943, -0.9583,  0.9970,  0.8655, -0.9027,  0.9991,  0.9996,  0.9302,\n",
+      "         0.8121, -0.9828, -0.6402,  0.9943, -0.9744,  0.9938,  0.9943,  0.9946,\n",
+      "         0.9839,  0.7040,  0.9864,  0.9982,  0.9653,  0.9998,  0.9920,  0.9784,\n",
+      "         0.9988,  0.9966,  0.9994,  0.9944,  0.9996,  0.9905,  0.8299,  0.9122,\n",
+      "         0.9917,  0.9949,  0.9833,  0.9912,  0.9570,  0.9853, -0.9927,  0.9703,\n",
+      "         0.9864,  0.0318,  0.4404,  0.9747, -0.9948,  0.9860,  0.9943,  0.9330,\n",
+      "         0.9932,  0.9994,  0.9998,  0.9904,  0.9635,  0.9971,  0.9996,  0.8853,\n",
+      "        -0.9378,  0.9992,  0.9753,  0.9997,  0.9925,  0.9943,  0.9975,  0.9958,\n",
+      "         0.9813,  0.9821,  0.9966,  0.9993,  0.9887,  0.9965,  0.9994,  0.3614,\n",
+      "         0.9973, -0.9772,  0.9683,  0.9805,  0.9967,  0.9876, -1.0000,  0.9993,\n",
+      "         0.9814,  0.9088,  0.8852,  0.9990,  0.9921,  0.9939,  0.8067,  0.9989,\n",
+      "         0.9994,  0.9997,  0.9991,  0.9305,  0.9993,  0.9956,  0.9954,  0.9965,\n",
+      "         0.9839,  0.9836,  0.9771,  0.9105,  0.9931,  0.9981, -0.7170,  0.9975,\n",
+      "        -0.9933,  0.9862,  0.9985,  0.9864,  0.2334,  0.9991,  0.9959,  0.9952,\n",
+      "         0.9853,  0.9868,  0.9983,  0.9952, -0.9288,  0.9975,  0.9988, -0.9569,\n",
+      "        -0.8888, -0.9877,  0.9765,  0.9275,  0.9941, -0.9216,  0.9995,  0.9962,\n",
+      "         0.9876,  0.9995,  0.9757,  0.9902,  0.9980,  0.9994, -0.5810,  0.9983,\n",
+      "         0.9967,  0.9211,  0.9729, -0.8414,  0.9602, -0.8967,  0.1608, -0.1039,\n",
+      "        -0.9412,  0.0535,  0.9929,  0.8147,  0.9994,  0.9744,  0.9992,  0.9969,\n",
+      "         0.9884,  0.9976,  0.3082,  0.9823,  0.9949, -0.5127,  0.9909, -0.9105,\n",
+      "        -0.9975,  0.9982,  0.9910,  0.9808,  0.9991,  0.2477,  0.9941,  0.9964,\n",
+      "         0.9970,  0.6560,  0.9962,  0.9962,  0.9966,  0.9963,  0.9930,  0.9903,\n",
+      "         0.9974,  0.9843,  0.9961,  0.8918,  0.9900,  0.9970,  0.9998,  0.9791,\n",
+      "         0.9974,  0.9710,  0.5504,  0.9937, -0.9263,  0.9974, -0.8524, -0.9995,\n",
+      "         0.9711,  0.3348,  0.9976,  0.9770,  0.9636,  0.9986,  0.9905,  0.9989,\n",
+      "         0.9995,  0.9973,  0.9970,  0.9969,  0.9997,  0.9949,  0.9754, -0.9891,\n",
+      "         0.9961, -0.0819,  0.9982,  0.9870,  0.9883, -0.9899,  0.9899,  0.9980,\n",
+      "         0.9780,  0.9974,  0.9944,  0.9685,  0.9982,  0.9809,  0.9992, -0.7607,\n",
+      "         0.9922,  0.9983,  0.9985,  0.9871,  0.9986,  0.9962,  0.9955, -0.9876,\n",
+      "         0.9984,  0.9979,  0.9997,  0.9980,  0.9101,  0.9930,  0.7889,  0.9934,\n",
+      "         0.9960,  0.9931,  0.9865,  0.9952, -0.2948,  0.9964, -0.6116, -0.9989,\n",
+      "        -0.9956,  0.9968,  0.9985,  0.9973,  0.8637,  0.9978, -0.9749,  0.9991,\n",
+      "         0.9985,  0.9984,  0.5852, -0.9869,  0.9879,  0.9979,  0.9990,  0.9977,\n",
+      "        -0.6244,  0.9969,  0.9942,  0.9788,  0.9991, -0.8973,  0.9980,  0.9917,\n",
+      "         0.9997,  0.9993,  0.9986,  0.9916, -0.2079,  0.9908,  0.9942,  0.9943,\n",
+      "         0.9949,  0.6439,  0.9958,  0.9659,  0.9978, -0.9312,  0.9966,  0.9771,\n",
+      "         0.9820,  0.4393,  0.9988, -0.9002,  0.9863,  0.9629,  0.5071,  0.9996,\n",
+      "        -0.5554,  0.9435,  0.9993, -0.3745,  0.9047,  0.9859,  0.9854,  0.9248,\n",
+      "         0.7041,  0.9940,  0.9954,  0.9889,  0.9995,  0.8817,  0.9918,  0.9636,\n",
+      "         0.9985,  0.9903,  0.9837,  0.8778,  0.8770,  0.9963,  0.9987,  0.9987,\n",
+      "         0.9969,  0.9182,  0.6843,  0.9994,  0.9964,  0.9998,  0.9913,  0.9861,\n",
+      "         0.9982,  0.9838,  0.9994,  0.9913,  0.9972,  0.9989,  0.9933,  0.9883,\n",
+      "        -0.9972,  0.9988,  0.9984, -0.7982,  0.9838,  0.9955,  0.9989, -0.9744,\n",
+      "         0.9743,  0.9879,  0.9975,  0.9895,  0.9988,  0.9906,  0.9958,  0.9402,\n",
+      "         0.9904,  0.9974,  0.4660,  0.9994,  0.9984,  0.9833,  0.9915,  0.9974],\n",
+      "       device='cuda:0')\n",
+      "tensor(0.8263, device='cuda:0')\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torchaudio\n",
+    "import torch\n",
+    "from speechbrain.pretrained import EncoderClassifier\n",
+    "cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)\n",
+    "classifier = EncoderClassifier.from_hparams(source=\"speechbrain/spkrec-xvect-voxceleb\", savedir=\"pretrained_models/spkrec-xvect-voxceleb\", run_opts={\"device\":\"cuda\"})\n",
+    "target_signal, fs =torchaudio.load('./audio/7729.wav')\n",
+    "target_signal_embeddings = classifier.encode_batch(target_signal).squeeze()\n",
+    "for mels, texts, audio in tqdm(loader):\n",
+    "    embedding = classifier.encode_batch(audio).squeeze()\n",
+    "    print(cos(embedding, target_signal_embeddings))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "b36ade7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import matplotlib\n",
+    "from importlib import reload\n",
+    "plt.style.use('ggplot')\n",
+    "matplotlib=reload(matplotlib)\n",
+    "plt.figure(figsize=(6,4.5))\n",
+    "x = [1,3,5,10,15]\n",
+    "y1 = [17.39,15.22,17.39,19.57,19.57]\n",
+    "y2 = [97.83,47.83,21.74,23.91,23.91]\n",
+    "plt.subplot(211)\n",
+    "plt.plot(x,y1,'--bo', label=\"Phoneme Hallucinator (Ours)\")\n",
+    "plt.plot(x,y2,'--r+', label=\"kNN-VC\")\n",
+    "plt.ylabel(\"Word Error Rate\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.xticks([])\n",
+    "plt.subplot(212)\n",
+    "y1 = [0.8077,0.8222,0.8203,0.8221,0.8241]\n",
+    "y2 = [0.7524,0.8235, 0.8255, 0.8242, 0.8263]\n",
+    "plt.plot(x,y1,'--bo', label=\"Phoneme Hallucinator (Ours)\")\n",
+    "plt.plot(x,y2,'--r+', label=\"kNN-VC\")\n",
+    "plt.xlabel(\"Target Voice Duration (s)\")\n",
+    "plt.ylabel(\"Speaker Similarity (cos)\")\n",
+    "plt.xticks(x)\n",
+    "#plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"knn-vc-ablation.pdf\",dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "243e3182",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Failed to open the input \"./amazing_sound.wav\" (No such file or directory).",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 7\u001b[0m\n\u001b[1;32m      5\u001b[0m classifier \u001b[38;5;241m=\u001b[39m EncoderClassifier\u001b[38;5;241m.\u001b[39mfrom_hparams(source\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspeechbrain/spkrec-xvect-voxceleb\u001b[39m\u001b[38;5;124m\"\u001b[39m, savedir\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpretrained_models/spkrec-xvect-voxceleb\u001b[39m\u001b[38;5;124m\"\u001b[39m, run_opts\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdevice\u001b[39m\u001b[38;5;124m\"\u001b[39m:\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m})\n\u001b[1;32m      6\u001b[0m target_signal, fs \u001b[38;5;241m=\u001b[39mtorchaudio\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./audio/7729_short.wav\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m our_signal, fs \u001b[38;5;241m=\u001b[39m\u001b[43mtorchaudio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m./amazing_sound.wav\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m freevc_signal, fs \u001b[38;5;241m=\u001b[39mtorchaudio\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./freevc.wav\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28mprint\u001b[39m(freevc_signal\u001b[38;5;241m.\u001b[39mshape)\n",
+      "File \u001b[0;32m~/anaconda3/envs/vc/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:256\u001b[0m, in \u001b[0;36mload\u001b[0;34m(filepath, frame_offset, num_frames, normalize, channels_first, format)\u001b[0m\n\u001b[1;32m    254\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    255\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ret\n\u001b[0;32m--> 256\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fallback_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframe_offset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_frames\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannels_first\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/vc/lib/python3.10/site-packages/torchaudio/io/_compat.py:107\u001b[0m, in \u001b[0;36mload_audio\u001b[0;34m(src, frame_offset, num_frames, convert, channels_first, format)\u001b[0m\n\u001b[1;32m     99\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_audio\u001b[39m(\n\u001b[1;32m    100\u001b[0m     src: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m    101\u001b[0m     frame_offset: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[38;5;28mformat\u001b[39m: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    106\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[torch\u001b[38;5;241m.\u001b[39mTensor, \u001b[38;5;28mint\u001b[39m]:\n\u001b[0;32m--> 107\u001b[0m     s \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclasses\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtorchaudio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mffmpeg_StreamReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    108\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _load_audio(s, frame_offset, num_frames, convert, channels_first)\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Failed to open the input \"./amazing_sound.wav\" (No such file or directory)."
+     ]
+    }
+   ],
+   "source": [
+    "import torchaudio\n",
+    "import torch\n",
+    "from speechbrain.pretrained import EncoderClassifier\n",
+    "cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)\n",
+    "classifier = EncoderClassifier.from_hparams(source=\"speechbrain/spkrec-xvect-voxceleb\", savedir=\"pretrained_models/spkrec-xvect-voxceleb\", run_opts={\"device\":\"cuda\"})\n",
+    "target_signal, fs =torchaudio.load('./audio/7729_short.wav')\n",
+    "our_signal, fs =torchaudio.load('./amazing_sound.wav')\n",
+    "freevc_signal, fs =torchaudio.load('./freevc.wav')\n",
+    "target_signal_embeddings = classifier.encode_batch(target_signal).squeeze()\n",
+    "our_signal_embeddings = classifier.encode_batch(our_signal).squeeze()\n",
+    "freevc_signal_embeddings = classifier.encode_batch(freevc_signal).squeeze()\n",
+    "print(cos(our_signal_embeddings, target_signal_embeddings))\n",
+    "print(cos(freevc_signal_embeddings, target_signal_embeddings))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3dd5402f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                              | 0/9500 [00:00<?, ?it/s]/home/leo/anaconda3/envs/vc/lib/python3.10/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\n",
+      "Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at /opt/conda/conda-bld/pytorch_1678402411778/work/aten/src/ATen/native/SpectralOps.cpp:862.)\n",
+      "  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]\n",
+      "100%|███████████████████████████████████| 9500/9500 [03:58<00:00, 39.89it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.472\n",
+      "0.3551578947368421\n",
+      "0.9540138582367647 0.010178320460466229\n",
+      "0.9460661411410883 0.011913444046103392\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "import numpy as np\n",
+    "import sklearn.metrics\n",
+    "\n",
+    "def compute_eer(label, pred):\n",
+    "    # all fpr, tpr, fnr, fnr, threshold are lists (in the format of np.array)\n",
+    "    fpr, tpr, threshold = sklearn.metrics.roc_curve(label, pred)\n",
+    "    fnr = 1 - tpr\n",
+    "\n",
+    "    # the threshold of fnr == fpr\n",
+    "    eer_threshold = threshold[np.nanargmin(np.absolute((fnr - fpr)))]\n",
+    "\n",
+    "    # theoretically eer from fpr and eer from fnr should be identical but they can be slightly differ in reality\n",
+    "    eer_1 = fpr[np.nanargmin(np.absolute((fnr - fpr)))]\n",
+    "    eer_2 = fnr[np.nanargmin(np.absolute((fnr - fpr)))]\n",
+    "\n",
+    "    # return the mean of eer from fpr and from fnr\n",
+    "    eer = (eer_1 + eer_2) / 2\n",
+    "    return eer\n",
+    "\n",
+    "\n",
+    "class SpeechData(torch.utils.data.Dataset):\n",
+    "    def __init__(self, device=DEVICE):\n",
+    "        self.device = device\n",
+    "        self.dataset = []\n",
+    "        self.ours_data_path = \"/media/leo/OS/knn-vc-master/eval/expand_result_XXL_cond_unpeq_100k_nondenoise\"\n",
+    "        self.freevc_data_path = \"/media/leo/work/projects/FreeVC/eval\"\n",
+    "        self.target_path = \"/media/leo/OS/knn-vc-master/eval/data_short\"\n",
+    "        #self.target_path = \"/media/leo/OS/knn-vc-master/eval/data\"\n",
+    "        for each in os.listdir(self.ours_data_path):\n",
+    "            self.dataset.append(os.path.join(self.ours_data_path, each))\n",
+    "        self.p = re.compile(\".*to_{(.*)}_{(.*)}.*.wav\")\n",
+    "        \n",
+    "    def __len__(self):\n",
+    "        return len(self.dataset)\n",
+    "    \n",
+    "    def __getitem__(self, item):\n",
+    "        path = self.dataset[item]\n",
+    "        ours_filename = os.path.basename(path)\n",
+    "        freevc_filename = os.path.join(self.freevc_data_path, ours_filename)\n",
+    "        result = self.p.search(path)\n",
+    "        spk = result.group(1)\n",
+    "        utt = result.group(2)\n",
+    "        tgt_filename = os.path.join(self.target_path, spk, utt + \".flac\")\n",
+    "        tgt_others = os.listdir(os.path.join(self.target_path, spk))\n",
+    "        tgt_others.remove(utt + \".flac\")\n",
+    "        tgt_others = [each for each in tgt_others if '.txt' not in each]\n",
+    "        tgt_others = random.choice(tgt_others)\n",
+    "        tgt_others_filename = os.path.join(self.target_path, spk, tgt_others)\n",
+    "        ours_audio, sr = torchaudio.load(path)\n",
+    "        freevc_audio, _ = torchaudio.load(freevc_filename)\n",
+    "        tgt_audio, _ = torchaudio.load(tgt_filename)\n",
+    "        tgt_other_audio, _ = torchaudio.load(tgt_others_filename)\n",
+    "        assert sr == 16000\n",
+    "        return (ours_audio[0].to(self.device), freevc_audio[0].to(self.device), tgt_audio[0].to(self.device), tgt_other_audio[0].to(self.device))\n",
+    "\n",
+    "loader = torch.utils.data.DataLoader(SpeechData(), batch_size=1)\n",
+    "from speechbrain.pretrained import EncoderClassifier\n",
+    "cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)\n",
+    "classifier = EncoderClassifier.from_hparams(source=\"speechbrain/spkrec-xvect-voxceleb\", savedir=\"pretrained_models/spkrec-xvect-voxceleb\", run_opts={\"device\":\"cuda\"})\n",
+    "cos_ours = []\n",
+    "cos_freevc = []\n",
+    "cos_tgt = []\n",
+    "from tqdm import tqdm\n",
+    "for ours, freevc, tgt, tgt_other in tqdm(loader):\n",
+    "    ours_embed = classifier.encode_batch(ours).squeeze()\n",
+    "    freevc_embed = classifier.encode_batch(freevc).squeeze()\n",
+    "    tgt_embed = classifier.encode_batch(tgt).squeeze()\n",
+    "    tgt_others_embed = classifier.encode_batch(tgt_other).squeeze()\n",
+    "    \n",
+    "    cos_ours.append(cos(ours_embed, tgt_embed).item())\n",
+    "    cos_freevc.append(cos(freevc_embed, tgt_embed).item())\n",
+    "    cos_tgt.append(cos(tgt_embed, tgt_others_embed).item())\n",
+    "ours = cos_ours + cos_tgt\n",
+    "freevc = cos_freevc + cos_tgt\n",
+    "gt = [0] * len(cos_ours) + [1] * len(cos_tgt)\n",
+    "print(compute_eer(gt, ours))\n",
+    "print(compute_eer(gt, freevc))\n",
+    "print(np.mean(cos_ours),np.std(cos_ours))\n",
+    "print(np.mean(cos_freevc),np.std(cos_freevc))\n",
+    "\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "af66a70c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.4961052631578947\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.optimize import brentq\n",
+    "from scipy.interpolate import interp1d\n",
+    "\n",
+    "def calculate_eer(genuine_scores, impostor_scores):\n",
+    "    # Create a function to interpolate the Receiver Operating Characteristic (ROC) curve\n",
+    "    fpr, tpr, thresholds = roc_curve(genuine_scores, impostor_scores)\n",
+    "    roc_interp = interp1d(fpr, tpr)\n",
+    "\n",
+    "    # Find the point on the ROC curve closest to the coordinate (1, 1)\n",
+    "    eer_threshold = brentq(lambda x : 1. - x - roc_interp(x), 0., 1.)\n",
+    "    eer = roc_interp(eer_threshold)\n",
+    "\n",
+    "    return eer\n",
+    "print(calculate_eer(gt,ours))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ae85934",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Phoneme_Hallucinator_v2/evaluation/ASR.ipynb b/Phoneme_Hallucinator_v2/evaluation/ASR.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ceb827a0d6bdf610f5b2f45646d164ca60e133a4
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/evaluation/ASR.ipynb
@@ -0,0 +1,676 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "79af4de2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "\n",
+    "try:\n",
+    "    import tensorflow  # required in Colab to avoid protobuf compatibility issues\n",
+    "except ImportError:\n",
+    "    pass\n",
+    "\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "import whisper\n",
+    "import torchaudio\n",
+    "\n",
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "\n",
+    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "dabded15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SpeechData(torch.utils.data.Dataset):\n",
+    "    def __init__(self, device=DEVICE):\n",
+    "        self.device = device\n",
+    "        self.dataset = [\n",
+    "            \"./samples/knn-vc-ablation/amazing_sound_1s.wav\",\n",
+    "            \"./samples/knn-vc-ablation/amazing_sound_3s.wav\",\n",
+    "            \"./samples/knn-vc-ablation/amazing_sound_5s.wav\",\n",
+    "            \"./samples/knn-vc-ablation/amazing_sound_10s.wav\",\n",
+    "            \"./samples/knn-vc-ablation/amazing_sound_15s.wav\",\n",
+    "        ]\n",
+    "        self.text = \"THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRAWLING AND CREEPING AROUND HER SHE HAD STRUGGLED IN DUMB SPEECHLESS TERROR AGAINST SOME MIGHTY GRASPING THAT STROVE FOR HER LIFE WITH GNARLED AND CREEPING FINGERS BUT NOW AT LAST WEAKLY SHE OPENED HER EYES AND QUESTIONED\"\n",
+    "    \n",
+    "    def __len__(self):\n",
+    "        return len(self.dataset)\n",
+    "    \n",
+    "    def __getitem__(self, item):\n",
+    "        path = self.dataset[item]\n",
+    "        audio, sr = torchaudio.load(path)\n",
+    "        assert sr == 16000\n",
+    "        audio = whisper.pad_or_trim(audio.flatten()).to(self.device)\n",
+    "        mel = whisper.log_mel_spectrogram(audio)\n",
+    "        return (mel, self.text, audio)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "6e9792fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model is English-only and has 71,825,408 parameters.\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = torch.utils.data.DataLoader(SpeechData(), batch_size=1)\n",
+    "model = whisper.load_model(\"base.en\")\n",
+    "print(\n",
+    "    f\"Model is {'multilingual' if model.is_multilingual else 'English-only'} \"\n",
+    "    f\"and has {sum(np.prod(p.shape) for p in model.parameters()):,} parameters.\"\n",
+    ")\n",
+    "options = whisper.DecodingOptions(language=\"en\", without_timestamps=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "45e1dc8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00,  9.56it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "hypotheses = []\n",
+    "references = []\n",
+    "from tqdm import tqdm\n",
+    "for mels, texts, audio in tqdm(loader):\n",
+    "    results = model.decode(mels, options)\n",
+    "    hypotheses.extend([result.text for result in results])\n",
+    "    references.extend(texts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "028a4d4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['The',\n",
+       " \"In that in where old Henry Stone's hidden pawling and creeping around her, she struggled in Stone's speechless terror against someone I did grasping his jaw for her life, was gnawled and creeping fingers, but now with last, briefly, she opened her eyes and questioned.\",\n",
+       " 'In that in web of endless pains have been crawling and creeping around her, she has struggled in stone speechless terror against some mighty grasping that stroll for her life, was gnarled and creeping fingers, but now at last, weekly, she opened her eyes and questioned.',\n",
+       " 'In that in web of endless veins had been crawling and creeping around her, she struggled in bones speechless terror against some mighty grasping that stole for her life, was gnawled and creeping fingers, but now at last, weak weak, she opened her eyes and questioned.',\n",
+       " 'Then that in web of endless veins had been crawling and creeping around her, she had struggled in done speechless terror against somebody grasping this jaw for her life, was gnawled and creeping fingers, but now at last, weakly, she opened her eyes and questioned.']"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hypotheses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "dbe983b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRAWLING AND CREEPING AROUND HER SHE HAD STRUGGLED IN DUMB SPEECHLESS TERROR AGAINST SOME MIGHTY GRASPING THAT STROVE FOR HER LIFE WITH GNARLED AND CREEPING FINGERS BUT NOW AT LAST WEAKLY SHE OPENED HER EYES AND QUESTIONED',\n",
+       " 'THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRAWLING AND CREEPING AROUND HER SHE HAD STRUGGLED IN DUMB SPEECHLESS TERROR AGAINST SOME MIGHTY GRASPING THAT STROVE FOR HER LIFE WITH GNARLED AND CREEPING FINGERS BUT NOW AT LAST WEAKLY SHE OPENED HER EYES AND QUESTIONED',\n",
+       " 'THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRAWLING AND CREEPING AROUND HER SHE HAD STRUGGLED IN DUMB SPEECHLESS TERROR AGAINST SOME MIGHTY GRASPING THAT STROVE FOR HER LIFE WITH GNARLED AND CREEPING FINGERS BUT NOW AT LAST WEAKLY SHE OPENED HER EYES AND QUESTIONED',\n",
+       " 'THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRAWLING AND CREEPING AROUND HER SHE HAD STRUGGLED IN DUMB SPEECHLESS TERROR AGAINST SOME MIGHTY GRASPING THAT STROVE FOR HER LIFE WITH GNARLED AND CREEPING FINGERS BUT NOW AT LAST WEAKLY SHE OPENED HER EYES AND QUESTIONED',\n",
+       " 'THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRAWLING AND CREEPING AROUND HER SHE HAD STRUGGLED IN DUMB SPEECHLESS TERROR AGAINST SOME MIGHTY GRASPING THAT STROVE FOR HER LIFE WITH GNARLED AND CREEPING FINGERS BUT NOW AT LAST WEAKLY SHE OPENED HER EYES AND QUESTIONED']"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "references"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "763f73fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hypothesis</th>\n",
+       "      <th>reference</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>The</td>\n",
+       "      <td>THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>In that in where old Henry Stone's hidden pawl...</td>\n",
+       "      <td>THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>In that in web of endless pains have been craw...</td>\n",
+       "      <td>THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>In that in web of endless veins had been crawl...</td>\n",
+       "      <td>THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Then that in web of endless veins had been cra...</td>\n",
+       "      <td>THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          hypothesis  \\\n",
+       "0                                                The   \n",
+       "1  In that in where old Henry Stone's hidden pawl...   \n",
+       "2  In that in web of endless pains have been craw...   \n",
+       "3  In that in web of endless veins had been crawl...   \n",
+       "4  Then that in web of endless veins had been cra...   \n",
+       "\n",
+       "                                           reference  \n",
+       "0  THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...  \n",
+       "1  THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...  \n",
+       "2  THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...  \n",
+       "3  THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...  \n",
+       "4  THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.DataFrame(dict(hypothesis=hypotheses, reference=references))\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "c65328f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hypothesis</th>\n",
+       "      <th>reference</th>\n",
+       "      <th>hypothesis_clean</th>\n",
+       "      <th>reference_clean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>The</td>\n",
+       "      <td>THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...</td>\n",
+       "      <td>the</td>\n",
+       "      <td>the net and web of endless things had been cra...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>In that in where old Henry Stone's hidden pawl...</td>\n",
+       "      <td>THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...</td>\n",
+       "      <td>in that in where old henry stone is hidden paw...</td>\n",
+       "      <td>the net and web of endless things had been cra...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>In that in web of endless pains have been craw...</td>\n",
+       "      <td>THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...</td>\n",
+       "      <td>in that in web of endless pains have been craw...</td>\n",
+       "      <td>the net and web of endless things had been cra...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>In that in web of endless veins had been crawl...</td>\n",
+       "      <td>THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...</td>\n",
+       "      <td>in that in web of endless veins had been crawl...</td>\n",
+       "      <td>the net and web of endless things had been cra...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Then that in web of endless veins had been cra...</td>\n",
+       "      <td>THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...</td>\n",
+       "      <td>then that in web of endless veins had been cra...</td>\n",
+       "      <td>the net and web of endless things had been cra...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          hypothesis  \\\n",
+       "0                                                The   \n",
+       "1  In that in where old Henry Stone's hidden pawl...   \n",
+       "2  In that in web of endless pains have been craw...   \n",
+       "3  In that in web of endless veins had been crawl...   \n",
+       "4  Then that in web of endless veins had been cra...   \n",
+       "\n",
+       "                                           reference  \\\n",
+       "0  THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...   \n",
+       "1  THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...   \n",
+       "2  THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...   \n",
+       "3  THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...   \n",
+       "4  THE NET AND WEB OF ENDLESS THINGS HAD BEEN CRA...   \n",
+       "\n",
+       "                                    hypothesis_clean  \\\n",
+       "0                                                the   \n",
+       "1  in that in where old henry stone is hidden paw...   \n",
+       "2  in that in web of endless pains have been craw...   \n",
+       "3  in that in web of endless veins had been crawl...   \n",
+       "4  then that in web of endless veins had been cra...   \n",
+       "\n",
+       "                                     reference_clean  \n",
+       "0  the net and web of endless things had been cra...  \n",
+       "1  the net and web of endless things had been cra...  \n",
+       "2  the net and web of endless things had been cra...  \n",
+       "3  the net and web of endless things had been cra...  \n",
+       "4  the net and web of endless things had been cra...  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import jiwer\n",
+    "from whisper.normalizers import EnglishTextNormalizer\n",
+    "\n",
+    "normalizer = EnglishTextNormalizer()\n",
+    "data[\"hypothesis_clean\"] = [normalizer(text) for text in data[\"hypothesis\"]]\n",
+    "data[\"reference_clean\"] = [normalizer(text) for text in data[\"reference\"]]\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "69d07cfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WER: 97.83 %\n",
+      "WER: 47.83 %\n",
+      "WER: 21.74 %\n",
+      "WER: 23.91 %\n",
+      "WER: 23.91 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(5):\n",
+    "    wer = jiwer.wer(list(data[\"reference_clean\"])[i], list(data[\"hypothesis_clean\"])[i])\n",
+    "\n",
+    "    print(f\"WER: {wer * 100:.2f} %\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "545a1d85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 72.30it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([ 0.9981, -0.9844,  0.9353,  0.9083,  0.9383,  0.9480,  0.9983,  0.9970,\n",
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+      "        -0.9972,  0.9988,  0.9984, -0.7982,  0.9838,  0.9955,  0.9989, -0.9744,\n",
+      "         0.9743,  0.9879,  0.9975,  0.9895,  0.9988,  0.9906,  0.9958,  0.9402,\n",
+      "         0.9904,  0.9974,  0.4660,  0.9994,  0.9984,  0.9833,  0.9915,  0.9974],\n",
+      "       device='cuda:0')\n",
+      "tensor(0.8263, device='cuda:0')\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torchaudio\n",
+    "import torch\n",
+    "from speechbrain.pretrained import EncoderClassifier\n",
+    "cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)\n",
+    "classifier = EncoderClassifier.from_hparams(source=\"speechbrain/spkrec-xvect-voxceleb\", savedir=\"pretrained_models/spkrec-xvect-voxceleb\", run_opts={\"device\":\"cuda\"})\n",
+    "target_signal, fs =torchaudio.load('./audio/7729.wav')\n",
+    "target_signal_embeddings = classifier.encode_batch(target_signal).squeeze()\n",
+    "for mels, texts, audio in tqdm(loader):\n",
+    "    embedding = classifier.encode_batch(audio).squeeze()\n",
+    "    print(cos(embedding, target_signal_embeddings))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "b36ade7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import matplotlib\n",
+    "from importlib import reload\n",
+    "plt.style.use('ggplot')\n",
+    "matplotlib=reload(matplotlib)\n",
+    "plt.figure(figsize=(6,4.5))\n",
+    "x = [1,3,5,10,15]\n",
+    "y1 = [17.39,15.22,17.39,19.57,19.57]\n",
+    "y2 = [97.83,47.83,21.74,23.91,23.91]\n",
+    "plt.subplot(211)\n",
+    "plt.plot(x,y1,'--bo', label=\"Phoneme Hallucinator (Ours)\")\n",
+    "plt.plot(x,y2,'--r+', label=\"kNN-VC\")\n",
+    "plt.ylabel(\"Word Error Rate\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.xticks([])\n",
+    "plt.subplot(212)\n",
+    "y1 = [0.8077,0.8222,0.8203,0.8221,0.8241]\n",
+    "y2 = [0.7524,0.8235, 0.8255, 0.8242, 0.8263]\n",
+    "plt.plot(x,y1,'--bo', label=\"Phoneme Hallucinator (Ours)\")\n",
+    "plt.plot(x,y2,'--r+', label=\"kNN-VC\")\n",
+    "plt.xlabel(\"Target Voice Duration (s)\")\n",
+    "plt.ylabel(\"Speaker Similarity (cos)\")\n",
+    "plt.xticks(x)\n",
+    "#plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"knn-vc-ablation.pdf\",dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "243e3182",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(0.9423, device='cuda:0')\n",
+      "tensor(0.9309, device='cuda:0')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torchaudio\n",
+    "import torch\n",
+    "from speechbrain.pretrained import EncoderClassifier\n",
+    "cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)\n",
+    "classifier = EncoderClassifier.from_hparams(source=\"speechbrain/spkrec-xvect-voxceleb\", savedir=\"pretrained_models/spkrec-xvect-voxceleb\", run_opts={\"device\":\"cuda\"})\n",
+    "target_signal, fs =torchaudio.load('./audio/7729_short.wav')\n",
+    "our_signal, fs =torchaudio.load('./amazing_sound.wav')\n",
+    "freevc_signal, fs =torchaudio.load('./freevc.wav')\n",
+    "target_signal_embeddings = classifier.encode_batch(target_signal).squeeze()\n",
+    "our_signal_embeddings = classifier.encode_batch(our_signal).squeeze()\n",
+    "freevc_signal_embeddings = classifier.encode_batch(freevc_signal).squeeze()\n",
+    "print(cos(our_signal_embeddings, target_signal_embeddings))\n",
+    "print(cos(freevc_signal_embeddings, target_signal_embeddings))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3dd5402f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1800x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "sns.set(rc={'figure.figsize':(6,3),\"figure.dpi\":300, 'savefig.dpi':300})\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "clrs = sns.color_palette(\"husl\", 5)\n",
+    "x = [1,2,3,4,5,6,7,8,9,10]\n",
+    "ours_wer_mean = np.array([5.25, 5.23, 5.05, 4.99, 5.00, 4.99, 5.01, 4.98, 4.97,5.01])\n",
+    "ours_wer_std = np.array([5.78, 5.67, 5.45, 5.36, 5.45, 5.55, 5.52, 5.51, 5.55,5.54])\n",
+    "unexpand_wer_mean = np.array([225.74,225.74,45.92,30.68, 24.75, 21.19, 18.65, 16.80, 15.52, 14.65])\n",
+    "unexpand_wer_std = np.array([220.06,220.06,28.43, 22.95, 17.07, 15.27, 13.30, 15.50, 11.43, 11.60])\n",
+    "\n",
+    "with sns.axes_style(\"darkgrid\"):\n",
+    "    ax.plot(x, ours_wer_mean, label=\"Ours\", c=clrs[0])\n",
+    "    ax.fill_between(x, ours_wer_mean-ours_wer_std, ours_wer_mean+ours_wer_std ,alpha=0.3, facecolor=clrs[0])\n",
+    "    ax.plot(x, unexpand_wer_mean, label=\"kNN-VC\", c=clrs[2])\n",
+    "    ax.fill_between(x, unexpand_wer_mean-unexpand_wer_std, unexpand_wer_mean+unexpand_wer_std ,alpha=0.3, facecolor=clrs[2])\n",
+    "    ax.legend()\n",
+    "    #ax.set_yscale('log')\n",
+    "    ax.set(xlabel='Target Voice Duration (s)', ylabel='Word Error Rate (%)', ylim=(-5, 80))\n",
+    "    \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "850f942a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "sns.set(rc={'figure.figsize':(6,3),\"figure.dpi\":300, 'savefig.dpi':300})\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "clrs = sns.color_palette(\"husl\", 5)\n",
+    "x = [1,2,3,4,5,6,7,8,9,10]\n",
+    "\n",
+    "ours_sim_mean = np.array([0.9115, 0.9363, 0.9524, 0.9587, 0.96236, 0.961528, 0.96693, 0.96836, 0.96941, 0.970055])\n",
+    "ours_sim_std = np.array([0.0225, 0.0159, 0.0111, 0.0099, 0.00878,0.010178, 0.00740, 0.006872,0.006921,0.007041])\n",
+    "\n",
+    "unexpand_sim_mean = np.array([0.9285526076617994, 0.9189,0.9551, 0.9582,0.9600, 0.96152, 0.96228, 0.96276,0.96340,0.963978])\n",
+    "unexpand_sim_std = np.array([0.0189, 0.0203, 0.01215, 0.0112,0.01087,0.010178, 0.01012,0.01017,0.009887,0.009834])\n",
+    "\n",
+    "with sns.axes_style(\"darkgrid\"):\n",
+    "    ax.plot(x, ours_sim_mean, label=\"Ours\", c=clrs[0])\n",
+    "    ax.fill_between(x, ours_sim_mean-ours_sim_std, ours_sim_mean+ours_sim_std ,alpha=0.3, facecolor=clrs[0])\n",
+    "    ax.plot(x, unexpand_sim_mean, label=\"kNN-VC\", c=clrs[2])\n",
+    "    ax.fill_between(x, unexpand_sim_mean-unexpand_sim_std, unexpand_sim_mean+unexpand_sim_std ,alpha=0.3, facecolor=clrs[2])\n",
+    "    ax.legend()\n",
+    "    #ax.set_yscale('log')\n",
+    "    ax.set(xlabel='Target Voice Duration (s)', ylabel='Speaker Similarity (cos)')\n",
+    "    \n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Phoneme_Hallucinator_v2/evaluation/init b/Phoneme_Hallucinator_v2/evaluation/init
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/evaluation/init
@@ -0,0 +1 @@
+
diff --git a/Phoneme_Hallucinator_v2/exp/speech_XXL_cond/params.json b/Phoneme_Hallucinator_v2/exp/speech_XXL_cond/params.json
new file mode 100644
index 0000000000000000000000000000000000000000..dedbe55c3d48ea4be5e1b12d5a990b5813232a3b
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/exp/speech_XXL_cond/params.json
@@ -0,0 +1,33 @@
+{
+    "dataset": "speech",
+    "dimension": 1024,
+    "batch_size": 50,
+    "set_size": 200,
+    "mask_type": "arb_expand",
+    "model": "pc_acset_vae",
+    "latent_encoder_hidden": [256,256,256,256],
+    "latent_dim": 256,
+    "trans_params": {
+        "transform": ["L","LR","CP","R","L","LR","CP","R","L","LR","CP","R","L","LR","CP"],
+        "dimension": 256,
+        "coupling_hids": [256,256]
+    },
+    "vae_params": {
+    	"hid_dimensions": 256,
+    	"dimension": 1024,
+    	"enc_dense_hids": [512,512,512,512],
+    	"dec_dense_hids": [512,512,512,512]
+    },
+    "use_peq_embed": 1,
+    "set_xformer_hids": [256,256,256,256],
+    "epochs": 1000,
+    "optimizer": "adam",
+    "lr": 0.0001,
+    "decay_steps": 100000,
+    "decay_rate": 0.5,
+    "clip_gradient": 1,
+    "exp_dir": "Phoneme_Hallucinator_v2/exp/speech_XXL_cond",
+    "summ_freq": 100,
+    "eval_metrics": ["sam"]
+}
+
diff --git a/Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/checkpoint b/Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/checkpoint
new file mode 100644
index 0000000000000000000000000000000000000000..5c6f171f97df60a092cd680f2d090e274d190e73
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/checkpoint
@@ -0,0 +1,3 @@
+model_checkpoint_path: "last.ckpt"
+all_model_checkpoint_paths: "params.ckpt"
+all_model_checkpoint_paths: "last.ckpt"
diff --git a/Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/last.ckpt.data-00000-of-00001 b/Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/last.ckpt.data-00000-of-00001
new file mode 100644
index 0000000000000000000000000000000000000000..874b509a1acade42360b4b353b5e97d9c499dc54
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/last.ckpt.data-00000-of-00001
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f48a8699b9c871c3e4d8fa92c1f4e4c58c3c054d7f3620577286307b1cee9c22
+size 228403264
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new file mode 100644
index 0000000000000000000000000000000000000000..0e31b8fbe53cbaafef40a5649cef41fd10edb752
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/last.ckpt.meta
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+version https://git-lfs.github.com/spec/v1
+oid sha256:fc65091d5349de08462ecf43b05ff3ed9e07c4de97160bc81c858f99a2979411
+size 7340118
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+++ b/Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/params.ckpt.data-00000-of-00001
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a10bbba728a64b8c3697e3fe546a1fe1a1a666c609e021acb24a15ccf615c740
+size 228403264
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diff --git a/Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/params.ckpt.meta b/Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/params.ckpt.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b995ddd976a042f5c8a0789274367c9729845ec5
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/exp/speech_XXL_cond/weights/params.ckpt.meta
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:22bf81dcaf6d4079a9151a9e797c1d4f6e7e209d493dba1f1f29b7d1ba5c2f59
+size 7340118
diff --git a/Phoneme_Hallucinator_v2/models/__init__.py b/Phoneme_Hallucinator_v2/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5152716a11a47f42e54a3c73f14e898868cb2be7
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/models/__init__.py
@@ -0,0 +1,9 @@
+
+def get_model(hps):
+    if hps.model == 'pc_acset_vae':
+        from .pc_acset_vae import ACSetVAE
+        model = ACSetVAE(hps)
+    else:
+        raise ValueError()
+
+    return model
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diff --git a/Phoneme_Hallucinator_v2/models/base.py b/Phoneme_Hallucinator_v2/models/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..54885fe26522368f02fba7b157df0f0de420e883
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/models/base.py
@@ -0,0 +1,103 @@
+import logging
+from pprint import pformat
+import numpy as np
+import tensorflow as tf
+class BaseModel(object):
+    def __init__(self, hps):
+        super(BaseModel, self).__init__()
+
+        self.hps = hps
+        g = tf.Graph()
+        with g.as_default():
+            # open a session
+            config = tf.compat.v1.ConfigProto()
+            config.log_device_placement = True
+            config.allow_soft_placement = True
+            config.gpu_options.allow_growth = True
+            self.sess = tf.compat.v1.Session(config=config, graph=g)
+            # build model
+            self.build_net()
+            self.build_ops()
+            # initialize
+            self.sess.run(tf.compat.v1.global_variables_initializer())
+            self.saver = tf.compat.v1.train.Saver()
+            self.writer = tf.compat.v1.summary.FileWriter(self.hps.exp_dir + '/summary')
+            # logging
+            total_params = 0
+            trainable_variables = tf.compat.v1.trainable_variables()
+            logging.info('=' * 20)
+            logging.info("Variables:")
+            logging.info(pformat(trainable_variables))
+            for v in trainable_variables:
+                num_params = np.prod(v.get_shape().as_list())
+                total_params += num_params
+
+            logging.info("TOTAL TENSORS: %d TOTAL PARAMS: %f[M]" % (
+                len(trainable_variables), total_params / 1e6))
+            logging.info('=' * 20)
+
+    def save(self, filename='params'):
+        fname = f'{self.hps.exp_dir}/weights/{filename}.ckpt'
+        self.saver.save(self.sess, fname)
+
+    def load(self, filename='params'):
+        fname = f'{self.hps.exp_dir}/weights/{filename}.ckpt'
+        self.saver.restore(self.sess, fname)
+
+    def build_net(self):
+        raise NotImplementedError()
+
+    def build_ops(self):
+        # optimizer
+        self.global_step = tf.compat.v1.train.get_or_create_global_step()
+        learning_rate = tf.compat.v1.train.inverse_time_decay(
+            self.hps.lr, self.global_step,
+            self.hps.decay_steps, self.hps.decay_rate,
+            staircase=True)
+        warmup_lr = tf.compat.v1.train.inverse_time_decay(
+            0.001 * self.hps.lr, self.global_step,
+            self.hps.decay_steps, self.hps.decay_rate,
+            staircase=True)
+        learning_rate = tf.cond(pred=tf.less(self.global_step, 1000), true_fn=lambda: warmup_lr, false_fn=lambda: learning_rate)
+        tf.compat.v1.summary.scalar('lr', learning_rate)
+        if self.hps.optimizer == 'adam':
+            optimizer = tf.compat.v1.train.AdamOptimizer(
+                learning_rate=learning_rate,
+                beta1=0.9, beta2=0.999, epsilon=1e-08,
+                use_locking=False, name="Adam")
+        elif self.hps.optimizer == 'rmsprop':
+            optimizer = tf.compat.v1.train.RMSPropOptimizer(
+                learning_rate=learning_rate)
+        elif self.hps.optimizer == 'mom':
+            optimizer = tf.compat.v1.train.MomentumOptimizer(
+                learning_rate=learning_rate,
+                momentum=0.9)
+        else:
+            optimizer = tf.compat.v1.train.GradientDescentOptimizer(
+                learning_rate=learning_rate)
+
+        # regularization
+        l2_reg = sum(
+                [tf.reduce_sum(input_tensor=tf.square(v)) for v in tf.compat.v1.trainable_variables()
+                 if ("magnitude" in v.name) or ("rescaling_scale" in v.name)])
+        reg_loss = 0.00005 * l2_reg
+
+        # train
+        grads_and_vars = optimizer.compute_gradients(
+            self.loss+reg_loss, tf.compat.v1.trainable_variables())
+        grads, vars_ = zip(*grads_and_vars)
+        if self.hps.clip_gradient > 0:
+            grads, gradient_norm = tf.clip_by_global_norm(
+                grads, clip_norm=self.hps.clip_gradient)
+            gradient_norm = tf.debugging.check_numerics(
+                gradient_norm, "Gradient norm is NaN or Inf.")
+            tf.compat.v1.summary.scalar('gradient_norm', gradient_norm)
+        capped_grads_and_vars = zip(grads, vars_)
+        self.train_op = optimizer.apply_gradients(
+            capped_grads_and_vars, global_step=self.global_step)
+        
+        # summary
+        self.summ_op = tf.compat.v1.summary.merge_all()
+
+    def execute(self, cmd, batch):
+        return self.sess.run(cmd, {self.x:batch['x'], self.b:batch['b'], self.m:batch['m']})
diff --git a/Phoneme_Hallucinator_v2/models/cVAE.py b/Phoneme_Hallucinator_v2/models/cVAE.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0b7e10d530ba889b1e74dc5f8b773122645e7da
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/models/cVAE.py
@@ -0,0 +1,45 @@
+import numpy as np
+import tensorflow as tf
+import tensorflow_probability as tfp
+tfd = tfp.distributions
+
+from .networks import dense_nn, cond_dense_nn
+
+class CondVAE(object):
+    def __init__(self, hps, name="cvae"):
+        self.hps = hps
+        self.name = name
+
+    def enc(self, x, cond=None):
+        '''
+        x: [B, C]
+        cond: [B, C]
+        '''
+        B,C = tf.shape(input=x)[0], tf.shape(input=x)[1]
+        with tf.compat.v1.variable_scope(self.name, reuse=tf.compat.v1.AUTO_REUSE):
+            prior_dist = tfd.MultivariateNormalDiag(tf.zeros(self.hps['hid_dimensions']),tf.ones(self.hps['hid_dimensions']))
+            if cond is None:
+                x = dense_nn(x, self.hps['enc_dense_hids'], 2 * self.hps['hid_dimensions'], False, "enc")
+            else:
+                x = cond_dense_nn(x, cond, self.hps['enc_dense_hids'], 2 * self.hps['hid_dimensions'], False, "enc")
+            m, s = x[:, :self.hps['hid_dimensions']], tf.nn.softplus(x[:, self.hps['hid_dimensions']:])
+            posterior_dist = tfd.MultivariateNormalDiag(m,s)
+            #kl = 0.5 * tf.reduce_sum(s + m ** 2 - 1.0 - tf.log(s), axis=-1)
+            kl = - tfd.kl_divergence(posterior_dist, prior_dist)
+            eps = prior_dist.sample(B)
+            posterior_sample = m + eps * s
+        return kl, posterior_sample
+    
+    def dec(self, x, cond=None):
+        '''
+        x: [B, C]
+        '''
+        B,C = tf.shape(input=x)[0], tf.shape(input=x)[1]
+        with tf.compat.v1.variable_scope(self.name, reuse=tf.compat.v1.AUTO_REUSE):
+            if cond is None:
+                x = dense_nn(x, self.hps['dec_dense_hids'], 2 * self.hps['dimension'], False, "dec")
+            else:
+                x = cond_dense_nn(x, cond, self.hps['dec_dense_hids'], 2 * self.hps['dimension'], False, "dec")
+            m, s = x[:, :self.hps['dimension']], tf.nn.softplus(x[:, self.hps['dimension']:])
+            sample_dist = tfd.MultivariateNormalDiag(loc=m, scale_diag=s)
+        return sample_dist
\ No newline at end of file
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diff --git a/Phoneme_Hallucinator_v2/models/flow/transforms.py b/Phoneme_Hallucinator_v2/models/flow/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..92b8563a962885d2da1b7f8666472f72c987394f
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/models/flow/transforms.py
@@ -0,0 +1,246 @@
+import tensorflow as tf
+tfk = tf.keras
+import numpy as np
+
+# base class
+class BaseTransform(object):
+    def __init__(self, hps, name='base'):
+        self.name = name
+        self.hps = hps
+
+        self.build()
+
+    def build(self):
+        pass
+
+    def forward(self, x):
+        raise NotImplementedError()
+
+    def inverse(self, z):
+        raise NotImplementedError()
+
+
+class Transform(BaseTransform):
+    def __init__(self, hps, name='transform'):
+        super(Transform, self).__init__(hps, name)
+
+    def build(self):
+        self.modules = []
+        with tf.compat.v1.variable_scope(self.name, reuse=tf.compat.v1.AUTO_REUSE):
+            for i, name in enumerate(self.hps.transform):
+                m = TRANS[name](self.hps, f'{i}')
+                self.modules.append(m)
+
+    def forward(self, x):
+        with tf.compat.v1.variable_scope(self.name, reuse=tf.compat.v1.AUTO_REUSE):
+            logdet = 0.
+            for module in self.modules:
+                x, ldet = module.forward(x)
+                logdet = logdet + ldet
+
+        return x, logdet
+
+    def inverse(self, z):
+        with tf.compat.v1.variable_scope(self.name, reuse=tf.compat.v1.AUTO_REUSE):
+            logdet = 0.
+            for module in reversed(self.modules):
+                z, ldet = module.inverse(z)
+                logdet = logdet + ldet
+
+        return z, logdet
+
+
+class Reverse(BaseTransform):
+    def __init__(self, hps, name):
+        name = f'reverse_{name}'
+        super(Reverse, self).__init__(hps, name)
+
+    def forward(self, x):
+        z = tf.reverse(x, [-1])
+        ldet = 0.0
+
+        return z, ldet
+
+    def inverse(self, z):
+        x = tf.reverse(z, [-1])
+        ldet = 0.0
+
+        return x, ldet
+
+
+class LeakyReLU(BaseTransform):
+    def __init__(self, hps, name):
+        name = f'lrelu_{name}'
+        super(LeakyReLU, self).__init__(hps, name)
+
+    def build(self):
+        with tf.compat.v1.variable_scope(self.name, reuse=tf.compat.v1.AUTO_REUSE):
+            self.alpha = tf.nn.sigmoid(
+                tf.compat.v1.get_variable('log_alpha', 
+                                initializer=5.0, 
+                                dtype=tf.float32))
+
+    def forward(self, x):
+        num_negative = tf.reduce_sum(input_tensor=tf.cast(tf.less(x, 0.0), tf.float32), axis=1)
+        ldet = num_negative * tf.math.log(self.alpha)
+        z = tf.maximum(x, self.alpha * x)
+
+        return z, ldet
+
+    def inverse(self, z):
+        num_negative = tf.reduce_sum(input_tensor=tf.cast(tf.less(z, 0.0), tf.float32), axis=1)
+        ldet = -1. * num_negative * tf.math.log(self.alpha)
+        x = tf.minimum(z, z / self.alpha)
+
+        return x, ldet
+
+
+class Coupling(BaseTransform):
+    def __init__(self, hps, name):
+        name = f'coupling_{name}'
+        super(Coupling, self).__init__(hps, name)
+
+    def build(self):
+        d = self.hps.dimension
+        with tf.compat.v1.variable_scope(self.name, reuse=tf.compat.v1.AUTO_REUSE):
+            self.net1 = tfk.Sequential(name=f'{self.name}/ms1')
+            for i, h in enumerate(self.hps.coupling_hids):
+                self.net1.add(tfk.layers.Dense(h, activation=tf.nn.tanh, name=f'l{i}'))
+            self.net1.add(tfk.layers.Dense(d, name=f'l{i+1}', kernel_initializer=tf.compat.v1.zeros_initializer()))
+
+            self.net2 = tfk.Sequential(name=f'{self.name}/ms2')
+            for i, h in enumerate(self.hps.coupling_hids):
+                self.net2.add(tfk.layers.Dense(h, activation=tf.nn.tanh, name=f'l{i}'))
+            self.net2.add(tfk.layers.Dense(d, name=f'l{i+1}', kernel_initializer=tf.compat.v1.zeros_initializer()))
+
+    def forward(self, x):
+        B = tf.shape(input=x)[0]
+        d = self.hps.dimension
+        ldet = tf.zeros(B, dtype=tf.float32)
+        # part 1
+        inp, out = x[:,::2], x[:,1::2]
+        scale, shift = tf.split(self.net1(inp), 2, axis=1)
+        out = (out + shift) * tf.exp(scale)
+        x = tf.reshape(tf.stack([inp,out],axis=-1), [B,d])
+        ldet = ldet + tf.reduce_sum(input_tensor=scale, axis=1)
+        # part 2
+        out, inp = x[:,::2], x[:,1::2]
+        scale, shift = tf.split(self.net2(inp), 2, axis=1)
+        out = (out + shift) * tf.exp(scale)
+        x = tf.reshape(tf.stack([out, inp],axis=-1), [B,d])
+        ldet = ldet + tf.reduce_sum(input_tensor=scale, axis=1)
+
+        return x, ldet
+
+    def inverse(self, z):
+        B = tf.shape(input=z)[0]
+        d = self.hps.dimension
+        ldet = tf.zeros(B, dtype=tf.float32)
+        # part 2
+        out, inp = z[:,::2], z[:,1::2]
+        scale, shift = tf.split(self.net2(inp), 2, axis=1)
+        out = out * tf.exp(-scale) - shift
+        z = tf.reshape(tf.stack([out, inp],axis=-1), [B,d])
+        ldet = ldet - tf.reduce_sum(input_tensor=scale, axis=1)
+        # part 1
+        inp, out = z[:,::2], z[:,1::2]
+        scale, shift = tf.split(self.net1(inp), 2, axis=1)
+        out = out * tf.exp(-scale) - shift
+        z = tf.reshape(tf.stack([inp, out], axis=-1), [B,d])
+        ldet = ldet - tf.reduce_sum(input_tensor=scale, axis=1)
+
+        return z, ldet
+
+
+class LULinear(BaseTransform):
+    def __init__(self, hps, name):
+        name = f'linear_{name}'
+        super(LULinear, self).__init__(hps, name)
+
+    def build(self):
+        d = self.hps.dimension
+        with tf.compat.v1.variable_scope(self.name, reuse=tf.compat.v1.AUTO_REUSE):
+            np_w = np.eye(d).astype("float32")
+            self.w = tf.compat.v1.get_variable('W', initializer=np_w)
+            self.b = tf.compat.v1.get_variable('b', initializer=tf.zeros([d]))
+
+    def get_LU(self):
+        d = self.hps.dimension
+        W = self.w
+        U = tf.linalg.band_part(W, 0, -1)
+        L = tf.eye(d) + W - U
+        A = tf.matmul(L, U)
+
+        return A, L, U
+
+    def forward(self, x):
+        A, L, U = self.get_LU()
+        ldet = tf.reduce_sum(input_tensor=tf.math.log(tf.abs(tf.linalg.diag_part(U))))
+        z = tf.matmul(x, A) + self.b
+
+        return z, ldet
+
+    def inverse(self, z):
+        B = tf.shape(input=z)[0]
+        A, L, U = self.get_LU()
+        ldet = -1 * tf.reduce_sum(input_tensor=tf.math.log(tf.abs(tf.linalg.diag_part(U))))
+        Ut = tf.tile(tf.expand_dims(tf.transpose(a=U, perm=[1, 0]), axis=0), [B,1,1])
+        Lt = tf.tile(tf.expand_dims(tf.transpose(a=L, perm=[1, 0]), axis=0), [B,1,1])
+        zt = tf.expand_dims(z - self.b, -1)
+        sol = tf.linalg.triangular_solve(Ut, zt)
+        x = tf.linalg.triangular_solve(Lt, sol, lower=False)
+        x = tf.squeeze(x, axis=-1)
+
+        return x, ldet
+
+
+# register all modules
+TRANS = {
+    'CP': Coupling,
+    'R': Reverse,
+    'LR': LeakyReLU,
+    'L': LULinear,
+}
+
+
+if __name__ == '__main__':
+    from pprint import pformat
+    from easydict import EasyDict as edict
+
+    hps = edict()
+    hps.dimension = 8
+    hps.coupling_hids = [32,32]
+    hps.transform = ['L','LR','CP','R']
+
+    x_ph = tf.compat.v1.placeholder(tf.float32, [32,8])
+    
+    l1 = Transform(hps, '1')
+    l2 = Transform(hps, '2')
+    z, fdet1 = l1.forward(x_ph)
+    z, fdet2 = l2.forward(z)
+    fdet = fdet1 + fdet2
+
+    x, bdet2 = l2.inverse(z)
+    x, bdet1 = l1.inverse(x)
+    bdet = bdet1 + bdet2
+
+    err = tf.reduce_sum(input_tensor=tf.square(x_ph - x))
+    det = tf.reduce_sum(input_tensor=fdet + bdet)
+
+    loss = tf.reduce_sum(input_tensor=tf.square(z)) - fdet
+    train_op = tf.compat.v1.train.AdamOptimizer(0.0001).minimize(loss)
+
+    sess = tf.compat.v1.Session()
+    sess.run(tf.compat.v1.global_variables_initializer())
+
+    print('='*20)
+    print('Variables:')
+    print(pformat(tf.compat.v1.trainable_variables()))
+
+    for i in range(1000):
+        x_nda = np.random.randn(32,8)
+        feed_dict = {x_ph:x_nda}
+
+        res = sess.run([err,det], feed_dict)
+        print(f'err:{res[0]} det:{res[1]}')
+        sess.run(train_op, feed_dict)
\ No newline at end of file
diff --git a/Phoneme_Hallucinator_v2/models/networks.py b/Phoneme_Hallucinator_v2/models/networks.py
new file mode 100644
index 0000000000000000000000000000000000000000..df1a9632319bc98016c022f9d31714a5739c5098
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/models/networks.py
@@ -0,0 +1,134 @@
+import numpy as np
+import tensorflow as tf
+initializer = tf.compat.v1.random_normal_initializer(stddev=0.001)
+def dense_nn(x, dims, dim_out, norm=True, name='dense_nn'):
+    with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE):
+        for i, size in enumerate(dims):
+            x = tf.compat.v1.layers.dense(x, size, name=f'd{i}', kernel_initializer=initializer)
+            if norm:
+                x = tf.contrib.layers.layer_norm(x)
+            x = tf.nn.leaky_relu(x)
+        x = tf.compat.v1.layers.dense(x, dim_out, name='d_out', kernel_initializer=initializer)
+
+        return x
+    
+def cond_dense_nn(x, cond, dims, dim_out, norm=True, name='cond_dense_nn'):
+    with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE):
+        for i, size in enumerate(dims):
+            x = tf.compat.v1.layers.dense(x, size, name=f'd{i}', kernel_initializer=initializer)
+            if norm:
+                x = tf.contrib.layers.layer_norm(x)
+            x = tf.nn.leaky_relu(x)
+            c = tf.compat.v1.layers.dense(cond, size, name=f'c{i}', kernel_initializer=initializer)
+            x = tf.sigmoid(c) * x
+        x = tf.compat.v1.layers.dense(x, dim_out, name='d_out', kernel_initializer=initializer)
+        return x
+    
+def large_cond_dense_nn(x, cond, dims, dim_out, norm=True, name='cond_dense_nn'):
+    with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE):
+        for i, size in enumerate(dims):
+            x = tf.compat.v1.layers.dense(x, size, name=f'd{i}', kernel_initializer=initializer)
+            if norm:
+                x = tf.contrib.layers.layer_norm(x)
+            x = tf.nn.leaky_relu(x)
+            c = dense_nn(cond, [256,256], size, False, name=f'c{i}')
+            x = tf.sigmoid(c) * x
+        x = tf.compat.v1.layers.dense(x, dim_out, name='d_out')
+        return x
+    
+def res_block(input, dim, block_name):
+    x = tf.compat.v1.layers.dense(input, dim, name=f'{block_name}_1', kernel_initializer=initializer)
+    x = tf.nn.leaky_relu(x)
+    x = tf.compat.v1.layers.dense(input, dim, name=f'{block_name}_2', kernel_initializer=initializer)
+    x += input
+    x = tf.nn.leaky_relu(x)
+    return x
+
+    
+def cond_resnet(x, cond, dims, dim_out, norm=True, name='cond_resnet'):
+    with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE):
+        x = tf.compat.v1.layers.dense(x, dims[0], name='1', kernel_initializer=initializer)
+        for i, size in enumerate(dims):
+            x = res_block(x, size, block_name=f'res_block_{i}')
+            if norm:
+                x = tf.contrib.layers.layer_norm(x)
+            c = dense_nn(cond, [256,256], size, False, name=f'c{i}')
+            x = tf.sigmoid(c) * x
+        x = tf.compat.v1.layers.dense(x, dim_out, name='d_out', kernel_initializer=initializer)
+        return x
+
+
+def convnet(x, dims, dim_out, name='convnet'):
+    with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE):
+        for i, d in enumerate(dims):
+            x = tf.compat.v1.layers.conv2d(x, d, 3, padding='same', name=f'c{i}_1')
+            x = tf.contrib.layers.instance_norm(x)
+            x = tf.nn.leaky_relu(x)
+            x = tf.compat.v1.layers.conv2d(x, d, 3, padding='same', name=f'c{i}_2')
+            x = tf.contrib.layers.instance_norm(x)
+            x = tf.nn.leaky_relu(x)
+            x = tf.compat.v1.layers.max_pooling2d(x, 2, 2)
+        x = tf.compat.v1.layers.flatten(x)
+        x = tf.compat.v1.layers.dense(x, dim_out, name='d1')
+        x = tf.contrib.layers.instance_norm(x)
+        x = tf.nn.leaky_relu(x)
+        x = tf.compat.v1.layers.dense(x, dim_out, name='d2')
+
+    return x
+
+def peq_convnet(x, dims, dim_out, attention, name='peq_convnet'):
+    B,N,H,W,C = tf.shape(input=x)[0], tf.shape(input=x)[1], *x.get_shape().as_list()[2:]
+    with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE):
+        # downsample
+        x = tf.reshape(x, [-1,H,W,C])
+        for d in dims[:2]:
+            x = tf.compat.v1.layers.conv2d(x, d, 3, strides=(1,1), padding='same')
+            x = tf.contrib.layers.instance_norm(x)
+            x = tf.nn.leaky_relu(x)
+            x = tf.compat.v1.layers.conv2d(x, d, 3, strides=(2,2), padding='same')
+            x = tf.contrib.layers.instance_norm(x)
+            x = tf.nn.leaky_relu(x)
+            H, W, C = H//2, W//2, d
+        x = tf.reshape(x, [B,N,H,W,C])
+        # attention across set dimension
+        x = tf.reshape(tf.transpose(a=x, perm=[0,2,3,1,4]), [B*H*W,N,C])
+        rep = attention(x, x, x)
+        x += rep
+        x = tf.transpose(a=tf.reshape(x, [B,H,W,N,C]), perm=[0,3,1,2,4])
+        # downsample
+        x = tf.reshape(x, [-1,H,W,C])
+        for d in dims[2:]:
+            x = tf.compat.v1.layers.conv2d(x, d, 3, strides=(1,1), padding='same')
+            x = tf.contrib.layers.instance_norm(x)
+            x = tf.nn.leaky_relu(x)
+            x = tf.compat.v1.layers.conv2d(x, d, 3, strides=(2,2), padding='same')
+            x = tf.contrib.layers.instance_norm(x)
+            x = tf.nn.leaky_relu(x)
+            H, W, C = H//2, W//2, d
+        x = tf.compat.v1.layers.flatten(x)
+        x = tf.compat.v1.layers.dense(x, dim_out)
+        x = tf.contrib.layers.instance_norm(x)
+        x = tf.nn.leaky_relu(x)
+        x = tf.compat.v1.layers.dense(x, dim_out)
+        x = tf.reshape(x, [B,N,dim_out])
+
+    return x
+
+def peq_resblock(x, dim, attention, name='peq_resnet'):
+    B,N,H,W,C = tf.shape(input=x)[0], tf.shape(input=x)[1], *x.get_shape().as_list()[2:]
+    with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE):
+        res = tf.reshape(tf.transpose(a=x, perm=[0,2,3,1,4]), [B*H*W,N,C])
+        res = attention(res, res, res)
+        res = tf.transpose(a=tf.reshape(res, [B,H,W,N,C]), perm=[0,3,1,2,4])
+        res = tf.reshape(res, [B*N,H,W,C])
+        res = tf.compat.v1.layers.conv2d(res, dim, 3, strides=(1,1), padding='same')
+        res = tf.contrib.layers.instance_norm(res)
+        res = tf.nn.leaky_relu(res)
+        res = tf.compat.v1.layers.conv2d(res, dim, 3, strides=(1,1), padding='same')
+        res = tf.contrib.layers.instance_norm(res)
+        res = tf.reshape(res, [B,N,H,W,C])
+        x += res
+        x = tf.nn.leaky_relu(x)
+
+    return x
+
diff --git a/Phoneme_Hallucinator_v2/models/pc_acset_vae.py b/Phoneme_Hallucinator_v2/models/pc_acset_vae.py
new file mode 100644
index 0000000000000000000000000000000000000000..37cf11174764e864a4abc2b5004b2aa914e31248
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/models/pc_acset_vae.py
@@ -0,0 +1,104 @@
+import numpy as np
+import tensorflow as tf
+import tensorflow_probability as tfp
+tfd = tfp.distributions
+from easydict import EasyDict as edict
+
+from .base import BaseModel
+from .pc_encoder import *
+from .flow.transforms import Transform
+from .cVAE import CondVAE
+
+class ACSetVAE(BaseModel):
+    def __init__(self, hps):
+        self.prior_net = LatentEncoder(hps, name='prior')
+        self.posterior_net = LatentEncoder(hps, name='posterior')
+        self.cvae = CondVAE(hps.vae_params, name="cvae")
+        if hps.use_peq_embed:
+            self.peq_embed = SetXformer(hps)
+        super(ACSetVAE, self).__init__(hps)
+    
+    def build_net(self):
+        with tf.compat.v1.variable_scope('acset_vae', reuse=tf.compat.v1.AUTO_REUSE):
+            self.x = tf.compat.v1.placeholder(tf.float32, [None, self.hps.set_size, self.hps.dimension]) #[200, 1024]
+            self.b = tf.compat.v1.placeholder(tf.float32, [None, self.hps.set_size, self.hps.dimension])
+            self.m = tf.compat.v1.placeholder(tf.float32, [None, self.hps.set_size, self.hps.dimension])
+
+            # build transform
+            self.transform = Transform(edict(self.hps.trans_params))
+
+            # prior
+            prior_inputs = tf.concat([self.x*self.b, self.b], axis=-1) # [200, 2048]
+            prior = self.prior_net(prior_inputs) # [256]
+            prior_sample = prior.sample() # [256]
+            prior_sample, _ = self.transform.inverse(prior_sample) # [256]
+            # peq embedding
+            cm = peq_embed = None
+            if self.hps.use_peq_embed:
+                peq_embed = self.peq_embed(prior_inputs) # [200, 256]
+                C = peq_embed.get_shape().as_list()[-1] # 256
+                cm = tf.reshape(peq_embed, [-1,C]) # [256]
+            
+            # posterior
+            posterior_inputs = tf.concat([self.x*self.m, self.m], axis=-1) # [200, 2048]
+            posterior = self.posterior_net(posterior_inputs) # [256]
+            posterior_sample = posterior.sample() # [256]
+
+            # kl term
+            z_sample, logdet = self.transform.forward(posterior_sample) # [256], [1]
+            logp = tf.reduce_sum(input_tensor=prior.log_prob(z_sample), axis=1) + logdet # [256] -> [1]
+            kl = tf.reduce_sum(input_tensor=posterior.entropy(), axis=1) + logp # [256] -> [1]
+
+            # generator 
+            x = tf.reshape(self.x, [-1,self.hps.dimension]) # [1024]
+            b = tf.reshape(self.b, [-1,self.hps.dimension])
+            m = tf.reshape(self.m, [-1,self.hps.dimension])
+            cv = tf.reshape(tf.tile(tf.expand_dims(posterior_sample, axis=1), [1,self.hps.set_size,1]), [-1,self.hps.latent_dim]) # [256] -> [1, 256] -> [1, 200, 256] -> [256]
+            if not cm is None:
+                c = tf.concat([cv, cm], axis=-1) # [512]
+            else:
+                c = cv
+            # vector-wise posterior
+            vec_kl, vec_post_sample = self.cvae.enc(tf.concat([x, c], axis=-1), c) # [1], [256]
+            vec_kl = tf.reshape(vec_kl, [-1, self.hps.set_size]) # [200]
+            recon_dist = self.cvae.dec(tf.concat([vec_post_sample, c], axis=-1), c)
+            log_likel = recon_dist.log_prob(x) # [1]
+            log_likel = tf.reshape(log_likel, [-1,self.hps.set_size]) # [200]
+            self.set_metric = self.set_elbo = log_likel + tf.expand_dims(kl, axis=1) / self.hps.set_size # [200]
+            log_likel = tf.reduce_mean(input_tensor=log_likel, axis=1)
+            tf.compat.v1.summary.scalar('log_likel', tf.reduce_mean(input_tensor=log_likel))
+
+            # elbo
+            self.elbo = log_likel + kl / self.hps.set_size + tf.reduce_mean(input_tensor=vec_kl, axis=1)
+            self.metric = self.elbo
+            self.loss = tf.reduce_mean(input_tensor=-self.elbo)
+            tf.compat.v1.summary.scalar('loss', self.loss)
+
+            # sample
+            x = tf.reshape(self.x, [-1,self.hps.dimension])
+            b = tf.reshape(self.b, [-1,self.hps.dimension])
+            m = tf.reshape(self.m, [-1,self.hps.dimension])
+            cv = tf.reshape(tf.tile(tf.expand_dims(prior_sample, axis=1), [1,self.hps.set_size,1]), [-1,self.hps.latent_dim])
+            if not cm is None:
+                c = tf.concat([cv, cm], axis=-1)
+            else:
+                c = cv
+            vec_prior_sample = tf.random.normal(shape=tf.shape(input=vec_post_sample))
+            sample_dist = self.cvae.dec(tf.concat([vec_prior_sample, c], axis=-1), c)
+            log_likel = sample_dist.log_prob(x)
+            sample = sample_dist.sample()
+            self.sample = tf.reshape(sample, [-1, self.hps.set_size, self.hps.dimension])
+
+            # compress
+
+            self.log_likel = tf.reshape(log_likel, [-1,self.hps.set_size]) + vec_kl
+        
+        # total_params = 0
+        # for variable in tf.trainable_variables():
+        #     shape = variable.get_shape()
+        #     params = 1
+        #     for dim in shape:
+        #         params *= dim
+        #     total_params += params
+
+        # print("Total number of parameters: ", total_params)
\ No newline at end of file
diff --git a/Phoneme_Hallucinator_v2/models/pc_encoder.py b/Phoneme_Hallucinator_v2/models/pc_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..8910749b3c698b0e41a0093cb1c0f2ea3f604328
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/models/pc_encoder.py
@@ -0,0 +1,39 @@
+import numpy as np
+import tensorflow as tf
+import tensorflow_probability as tfp
+tfd = tfp.distributions
+
+from .set_transformer import set_transformer
+
+class LatentEncoder(object):
+    def __init__(self, hps, name='latent'):
+        self.hps = hps
+        self.name = name
+
+    def __call__(self, x):
+        '''
+        x: [B,N,C]
+        '''
+        B,N,C = tf.shape(input=x)[0], tf.shape(input=x)[1], *x.get_shape().as_list()[2:]
+        with tf.compat.v1.variable_scope(self.name, reuse=tf.compat.v1.AUTO_REUSE):
+            x = set_transformer(x, self.hps.latent_encoder_hidden, name='set_xformer')
+            x = tf.reduce_mean(input_tensor=x, axis=1)
+            x = tf.compat.v1.layers.dense(x, self.hps.latent_dim*2, name='d1')
+            x = tf.nn.leaky_relu(x)
+            x = tf.compat.v1.layers.dense(x, self.hps.latent_dim*2, name='d2')
+            m, s = x[...,:self.hps.latent_dim], tf.nn.softplus(x[...,self.hps.latent_dim:])
+            dist = tfd.Normal(loc=m, scale=s)
+
+        return dist
+
+
+class SetXformer(object):
+    def __init__(self, hps, name='set_xformer'):
+        self.hps = hps
+        self.name = name
+
+    def __call__(self, x):
+        with tf.compat.v1.variable_scope(self.name, reuse=tf.compat.v1.AUTO_REUSE):
+            x = set_transformer(x, self.hps.set_xformer_hids, name='set_xformer')
+
+        return x
\ No newline at end of file
diff --git a/Phoneme_Hallucinator_v2/models/runner.py b/Phoneme_Hallucinator_v2/models/runner.py
new file mode 100644
index 0000000000000000000000000000000000000000..c605942b3cb35b0d5e9f724835b58a63d3cbbc3d
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/models/runner.py
@@ -0,0 +1,214 @@
+import os
+import logging
+from collections import defaultdict
+import numpy as np
+import pickle
+import tensorflow as tf
+from pprint import pformat
+
+from .utils import visualize, plot_functions, plot_img_functions
+
+class Runner(object):
+    def __init__(self, args, model):
+        self.args = args
+        self.sess = model.sess
+        self.model = model
+
+    def set_dataset(self, trainset, validset, testset):
+        self.trainset = trainset
+        self.validset = validset
+        self.testset = testset
+
+    def train(self):
+        train_metrics = []
+        num_batches = self.trainset.num_batches
+        self.trainset.initialize()
+        for i in range(num_batches):
+            batch = self.trainset.next_batch()
+            metric, summ, step, _ = self.model.execute(
+                [self.model.metric, self.model.summ_op, 
+                self.model.global_step, self.model.train_op], 
+                batch)
+            if (self.args.summ_freq > 0) and (i % self.args.summ_freq == 0):
+                self.model.writer.add_summary(summ, step)
+            train_metrics.append(metric)
+        train_metrics = np.concatenate(train_metrics, axis=0)
+
+        return np.mean(train_metrics)
+
+    def valid(self):
+        valid_metrics = []
+        num_batches = self.validset.num_batches
+        self.validset.initialize()
+        for i in range(num_batches):
+            batch = self.validset.next_batch()
+            metric = self.model.execute(self.model.metric, batch)
+            valid_metrics.append(metric)
+        valid_metrics = np.concatenate(valid_metrics, axis=0)
+
+        return np.mean(valid_metrics)
+
+    def valid_mse(self):
+        valid_mse = []
+        num_batches = self.validset.num_batches
+        self.validset.initialize()
+        for i in range(num_batches):
+            batch = self.validset.next_batch()
+            sample = self.model.execute(self.model.sample, batch)
+            mse = np.mean(np.sum(np.square(sample-batch['x']), axis=tuple(range(2,sample.ndim))), axis=1)
+            valid_mse.append(mse)
+        valid_mse = np.concatenate(valid_mse, axis=0)
+
+        return np.mean(valid_mse)
+
+    def valid_chd(self):
+        pass
+
+    def valid_emd(self):
+        pass
+
+    def test(self):
+        test_metrics = []
+        num_batches = self.testset.num_batches
+        self.testset.initialize()
+        for i in range(num_batches):
+            batch = self.testset.next_batch()
+            metric = self.model.execute(self.model.metric, batch)
+            test_metrics.append(metric)
+        test_metrics = np.concatenate(test_metrics)
+
+        return np.mean(test_metrics)
+
+    def test_mse(self):
+        test_mse = []
+        num_batches = self.testset.num_batches
+        self.testset.initialize()
+        for i in range(num_batches):
+            batch = self.testset.next_batch()
+            sample = self.model.execute(self.model.sample, batch)
+            mse = np.mean(np.sum(np.square(sample-batch['x']), axis=tuple(range(2,sample.ndim))), axis=1)
+            test_mse.append(mse)
+        test_mse = np.concatenate(test_mse, axis=0)
+
+        return np.mean(test_mse)
+
+    def test_chd(self):
+        pass
+
+    def test_emd(self):
+        pass
+
+    def run(self):
+        logging.info('==== start training ====')
+        best_train_metric = -np.inf
+        best_valid_metric = -np.inf
+        best_test_metric = -np.inf
+        for epoch in range(self.args.epochs):
+            train_metric = self.train()
+            valid_metric = self.valid()
+            test_metric = self.test()
+            # save
+            if train_metric > best_train_metric:
+                best_train_metric = train_metric
+            if valid_metric > best_valid_metric:
+                best_valid_metric = valid_metric
+                self.model.save()
+            if test_metric > best_test_metric:
+                best_test_metric = test_metric
+            logging.info("Epoch %d, train: %.4f/%.4f, valid: %.4f/%.4f test: %.4f/%.4f" %
+                 (epoch, train_metric, best_train_metric, 
+                 valid_metric, best_valid_metric,
+                 test_metric, best_test_metric))
+            # evaluate
+            if epoch % 100 == 0:
+                logging.info('==== start evaluating ====')
+                self.evaluate(folder=f'{epoch}', load=False)
+            self.model.save('last')
+
+        # finish
+        logging.info('==== start evaluating ====')
+        self.evaluate(load=True)
+
+    def evaluate(self, folder='test', load=True):
+        save_dir = f'{self.args.exp_dir}/evaluate/{folder}/'
+        os.makedirs(save_dir, exist_ok=True)
+        if load: self.model.load()
+        
+        # # likelihood
+        if 'likel' in self.args.eval_metrics:
+            valid_likel = self.valid()
+            test_likel = self.test()
+            logging.info(f"likelihood => valid: {valid_likel} test: {test_likel}")
+
+        # # mse
+        if 'mse' in self.args.eval_metrics:
+            valid_mse = self.valid_mse()
+            test_mse = self.test_mse()
+            logging.info(f"mse => valid: {valid_mse} test: {test_mse}")
+
+        if 'chd' in self.args.eval_metrics:
+            valid_chd = self.valid_chd()
+            test_chd = self.test_chd()
+            logging.info(f"chd => valid: {valid_chd} test: {test_chd}")
+
+        if 'emd' in self.args.eval_metrics:
+            valid_emd = self.valid_emd()
+            test_emd = self.test_emd()
+            logging.info(f"emd => valid: {valid_emd} test: {test_emd}")
+
+        if 'sam' in self.args.eval_metrics:
+            # train set
+            self.trainset.initialize()
+            batch = self.trainset.next_batch()
+            train_sample = self.model.execute(self.model.sample, batch)
+            visualize(train_sample, batch, f'{save_dir}/train_sam')
+
+            # valid set
+            self.validset.initialize()
+            batch = self.validset.next_batch()
+            valid_sample = self.model.execute(self.model.sample, batch)
+            visualize(valid_sample, batch, f'{save_dir}/valid_sam')
+
+            # test set
+            self.testset.initialize()
+            batch = self.testset.next_batch()
+            test_sample = self.model.execute(self.model.sample, batch)
+            visualize(test_sample, batch, f'{save_dir}/test_sam')
+
+        if 'fns' in self.args.eval_metrics:
+            # train set
+            self.trainset.initialize()
+            batch = self.trainset.next_batch()
+            train_mean, train_std = self.model.execute([self.model.mean, self.model.std], batch)
+            plot_functions(train_mean, train_std, batch, f'{save_dir}/train_fn')
+
+            # valid set
+            self.validset.initialize()
+            batch = self.validset.next_batch()
+            valid_mean, valid_std = self.model.execute([self.model.mean, self.model.std], batch)
+            plot_functions(valid_mean, valid_std, batch, f'{save_dir}/valid_fn')
+
+            # test set
+            self.testset.initialize()
+            batch = self.testset.next_batch()
+            test_mean, test_std = self.model.execute([self.model.mean, self.model.std], batch)
+            plot_functions(test_mean, test_std, batch, f'{save_dir}/test_fn')
+
+        if 'imfns' in self.args.eval_metrics:
+            # train set
+            self.trainset.initialize()
+            batch = self.trainset.next_batch()
+            train_mean, train_std = self.model.execute([self.model.mean, self.model.std], batch)
+            plot_img_functions(train_mean, train_std, batch, f'{save_dir}/train_fn')
+
+            # valid set
+            self.validset.initialize()
+            batch = self.validset.next_batch()
+            valid_mean, valid_std = self.model.execute([self.model.mean, self.model.std], batch)
+            plot_img_functions(valid_mean, valid_std, batch, f'{save_dir}/valid_fn')
+
+            # test set
+            self.testset.initialize()
+            batch = self.testset.next_batch()
+            test_mean, test_std = self.model.execute([self.model.mean, self.model.std], batch)
+            plot_img_functions(test_mean, test_std, batch, f'{save_dir}/test_fn')
diff --git a/Phoneme_Hallucinator_v2/models/set_transformer.py b/Phoneme_Hallucinator_v2/models/set_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..244ebd93e7cf7e5ea29d350fd06100331d16b47d
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/models/set_transformer.py
@@ -0,0 +1,54 @@
+import numpy as np
+import tensorflow as tf
+
+def layer_norm(x):
+    mean = tf.reduce_mean(input_tensor=x, axis=[1,2], keepdims=True)
+    std = tf.math.reduce_std(x, axis=[1,2], keepdims=True)
+    x = (x - mean) / std
+    return x
+
+def set_attention(Q, K, dim, num_heads, name='set_attention'):
+    with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE):
+        q = tf.compat.v1.layers.dense(Q, dim, name='query')
+        k = tf.compat.v1.layers.dense(K, dim, name='key')
+        v = tf.compat.v1.layers.dense(K, dim, name='value')
+
+        q_ = tf.concat(tf.split(q, num_heads, axis=-1), axis=0)
+        k_ = tf.concat(tf.split(k, num_heads, axis=-1), axis=0)
+        v_ = tf.concat(tf.split(v, num_heads, axis=-1), axis=0)
+
+        logits = tf.matmul(q_, k_, transpose_b=True)/np.sqrt(dim) # [B*Nh,Nq,Nk]
+        A = tf.nn.softmax(logits, axis=-1)
+        o = q_ + tf.matmul(A, v_)
+        
+        o = tf.concat(tf.split(o, num_heads, axis=0), axis=-1)
+        # o = tf.contrib.layers.layer_norm(o)
+        o = o + tf.compat.v1.layers.dense(o, dim, activation=tf.nn.relu, name='output')
+        # o = tf.contrib.layers.layer_norm(o)
+
+    return o
+
+def set_transformer(inputs, layer_sizes, name, num_heads=4, num_inds=16):
+    with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE):
+        out = inputs
+        for i, size in enumerate(layer_sizes):
+            inds = tf.compat.v1.get_variable(f'inds_{i}', shape=[1,num_inds,size], dtype=tf.float32, trainable=True,
+                                    initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"))
+            inds = tf.tile(inds, [tf.shape(input=out)[0],1,1])
+            tmp = set_attention(inds, out, size, num_heads, name=f'self_attn_{i}_pre')
+            out = set_attention(out, tmp, size, num_heads, name=f'self_attn_{i}_post')
+
+    return out
+
+def set_pooling(inputs, name, num_heads=4):
+    B = tf.shape(input=inputs)[0]
+    N = tf.shape(input=inputs)[1]
+    d = inputs.get_shape().as_list()[-1]
+    with tf.compat.v1.variable_scope(name, reuse=tf.compat.v1.AUTO_REUSE):
+        seed = tf.compat.v1.get_variable('pool_seed', shape=[1,1,d], dtype=tf.float32, trainable=True, 
+                                initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"))
+        seed = tf.tile(seed, [B,1,1])
+        out = set_attention(seed, inputs, d, num_heads, name='pool_attn')
+        out = tf.squeeze(out, axis=1)
+
+    return out
diff --git a/Phoneme_Hallucinator_v2/models/utils.py b/Phoneme_Hallucinator_v2/models/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a21cf55464015e926fe801e237daa71647b39d0
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/models/utils.py
@@ -0,0 +1,116 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns; sns.set()
+
+
+def visualize(s, batch, prefix):
+    if len(s.shape) == 5:
+        x, b, m = batch['x'], batch['b'], batch['m']
+        im_visualize(s, x, b, m, prefix)
+    elif len(s.shape) == 3:
+        x, b, m = batch['x'], batch['b'], batch['m']
+        pc_visualize(s, x, b, m, prefix)
+    elif len(s.shape) == 4:
+        xc, yc, xt, yt = batch['xc'], batch['yc'], batch['xt'], batch['yt']
+        fn_visualize(s, xc, yc, xt, yt, prefix)
+    else:
+        raise ValueError()
+
+def im_visualize(s, x, b, m, prefix):
+    B,N,H,W,C = s.shape
+    for i in range(B):
+        ss, xx, bb, mm = s[i], x[i], b[i], m[i]
+        if ss.shape[-1] == 2: # kspace
+            C = 1
+            ss = np.expand_dims(np.absolute(np.fft.ifft2(np.fft.ifftshift(ss[...,0] + ss[...,1] * 1j, axes=(-2,-1)))), axis=-1)
+            ss = np.array(ss*255, dtype=np.uint8)
+            xx = np.expand_dims(np.absolute(np.fft.ifft2(np.fft.ifftshift(xx[...,0] + xx[...,1] * 1j, axes=(-2,-1)))), axis=-1)
+            xx = np.array(xx*255, dtype=np.uint8)
+            bb = bb[...,0:1]
+            mm = mm[...,0:1]
+        ss = np.transpose(ss, [1,0,2,3]).reshape(H,W*N,C).squeeze()
+        xx = np.transpose(xx, [1,0,2,3]).reshape(H,W*N,C).squeeze()
+        bb = np.transpose(bb, [1,0,2,3]).reshape(H,W*N,C).squeeze()
+        mm = np.transpose(mm, [1,0,2,3]).reshape(H,W*N,C).squeeze()
+        xm = xx * mm + (1-mm) * 128
+        xo = xx * bb + (1-bb) * 128
+        img = np.concatenate([xm, xo, ss]).astype(np.uint8)
+
+        plt.imsave(f'{prefix}_{i}.png', img)
+
+def pc_visualize(s, x, b, m, prefix):
+    B,N,C = s.shape
+    for i in range(B):
+        ss, xx, bb = s[i], x[i], b[i]
+        o = np.where(bb[:,0]==1)[0]
+        fig = plt.figure(figsize=(7.5, 2.5))
+        ax = fig.add_subplot(131, projection='3d')
+        ax.scatter(xx[:,0], xx[:,1], xx[:,2], c='g', s=5)
+        ax.axis('off')
+        ax.grid(False)
+        ax = fig.add_subplot(132, projection='3d')
+        ax.scatter(xx[o,0], xx[o,1], xx[o,2], c='g', s=5)
+        ax.axis('off')
+        ax.grid(False)
+        ax = fig.add_subplot(133, projection='3d')
+        ax.scatter(ss[:,0], ss[:,1], ss[:,2], c='g', s=5)
+        ax.axis('off')
+        ax.grid(False)
+        plt.savefig(f'{prefix}_{i}.png')
+        plt.close('all')
+
+def fn_visualize(s, xc, yc, xt, yt, prefix):
+    B,K,N,C = s.shape
+    for i in range(B):
+        ss, xxc, yyc, xxt, yyt = s[i], xc[i], yc[i], xt[i], yt[i]
+        fig = plt.figure(figsize=(4.0, 2.5*K))
+        for k in range(K):
+            ax = fig.add_subplot(K,1,k+1)
+            ax.plot(xxc[k], yyc[k], 'rx', markersize=8)
+            ax.plot(xxt[k], yyt[k], 'ko', markersize=3)
+            ax.plot(xxt[k], ss[k], 'bo', markersize=3)
+        plt.savefig(f'{prefix}_{i}.png')
+        plt.close('all')
+
+
+def plot_functions(m, s, batch, prefix):
+    B,K,N,C = m.shape
+    xc, yc, xt, yt = batch['xc'], batch['yc'], batch['xt'], batch['yt']
+    for i in range(B):
+        mm, ss, xxc, yyc, xxt, yyt = m[i,:,:,0], s[i,:,:,0], xc[i,:,:,0], yc[i,:,:,0], xt[i,:,:,0], yt[i,:,:,0]
+        fig = plt.figure(figsize=(4.0, 2.5*K))
+        for k in range(K):
+            idx = np.argsort(xxt[k])
+            ax = fig.add_subplot(K,1,k+1)
+            ax.plot(xxc[k], yyc[k], 'rx', markersize=8)
+            ax.plot(xxt[k], yyt[k], 'ko', markersize=3)
+            ax.plot(xxt[k,idx], mm[k,idx], 'b', linewidth=2)
+            plt.fill_between(
+                xxt[k,idx],
+                mm[k,idx] - ss[k,idx],
+                mm[k,idx] + ss[k,idx],
+                alpha=0.2,
+                facecolor='#65c9f7',
+                interpolate=True)
+        plt.savefig(f'{prefix}_{i}.png')
+        plt.close('all')
+
+def plot_img_functions(m, s, batch, prefix):
+    B,K,N,C = m.shape
+    idx, xc, yc, xt, yt = batch['idx'], batch['xc'], batch['yc'], batch['xt'], batch['yt']
+    yo = np.ones_like(yt) * 128
+    yo[:,:,idx] = (yc + 0.5) * 255.
+    yt =  (yt + 0.5) * 255.
+    m = (m + 0.5) * 255.
+    for i in range(B):
+        yoi, yti, mi = yo[i], yt[i], m[i]
+        yoi = np.reshape(yoi, [K,28,28]).astype(np.uint8)
+        yoi = np.reshape(np.transpose(yoi, [1,0,2]), [28, K*28])
+        yti = np.reshape(yti, [K,28,28]).astype(np.uint8)
+        yti = np.reshape(np.transpose(yti, [1,0,2]), [28, K*28])
+        mi = np.reshape(mi, [K,28,28]).astype(np.uint8)
+        mi = np.reshape(np.transpose(mi, [1,0,2]), [28, K*28])
+        img = np.concatenate([yoi, mi, yti], axis=0)
+
+        plt.imsave(f'{prefix}_{i}.png', img)
+
diff --git a/Phoneme_Hallucinator_v2/scripts/__pycache__/speech_expansion_cls.cpython-310.pyc b/Phoneme_Hallucinator_v2/scripts/__pycache__/speech_expansion_cls.cpython-310.pyc
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diff --git a/Phoneme_Hallucinator_v2/scripts/__pycache__/speech_expansion_ins.cpython-310.pyc b/Phoneme_Hallucinator_v2/scripts/__pycache__/speech_expansion_ins.cpython-310.pyc
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diff --git a/Phoneme_Hallucinator_v2/scripts/run.py b/Phoneme_Hallucinator_v2/scripts/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3e12b1c77262245cd39bf0f2eaeb48fe001b0b4
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/scripts/run.py
@@ -0,0 +1,68 @@
+import os
+import sys
+p = os.path.split(os.path.dirname(os.path.abspath(__file__)))[0]
+sys.path.append(p)
+import ast
+import logging
+import argparse
+import numpy as np
+import tensorflow as tf
+from pprint import pformat
+
+from utils.hparams import HParams
+from models.runner import Runner
+from models import get_model
+from datasets import get_dataset
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--cfg_file', type=str)
+parser.add_argument('--config', action='append', default=[])
+parser.add_argument('--mode', type=str, default=None)
+parser.add_argument('--seed', type=int, default=1234)
+parser.add_argument('--gpu', type=str, default='0')
+args = parser.parse_args()
+params = HParams(args.cfg_file)
+# parse config
+for kv in args.config:
+    k, v = kv.split('=', maxsplit=1)
+    assert k, "Config item can't have empty key"
+    assert v, "Config item can't have empty value"
+    try:
+        v = ast.literal_eval(v)
+    except ValueError:
+        v = str(v)
+    params.update({k: v})
+for handler in logging.root.handlers[:]:
+    logging.root.removeHandler(handler)
+logging.basicConfig(filename=params.exp_dir + f'/{args.mode}.log',
+                    filemode='w',
+                    level=logging.INFO,
+                    format='%(asctime)-15s %(message)s')
+logging.info(pformat(params.dict))
+
+os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
+np.random.seed(args.seed)
+tf.compat.v1.set_random_seed(args.seed)
+# data
+trainset = get_dataset(params, 'train')
+validset = get_dataset(params, 'valid')
+testset = get_dataset(params, 'test')
+logging.info((f"trainset: {trainset.size}",
+              f"validset: {validset.size}",
+              f"testset: {testset.size}"))
+
+# model
+model = get_model(params)
+runner = Runner(params, model)
+runner.set_dataset(trainset, validset, testset)
+
+# run
+if args.mode == 'train':
+    runner.run()
+elif args.mode == 'resume':
+    model.load()
+    runner.run()
+elif args.mode == 'test':
+    runner.evaluate()
+else:
+    raise ValueError()
diff --git a/Phoneme_Hallucinator_v2/scripts/speech_expansion.py b/Phoneme_Hallucinator_v2/scripts/speech_expansion.py
new file mode 100644
index 0000000000000000000000000000000000000000..076f8f6e22fef0bd4886a33358cb942649e8d955
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/scripts/speech_expansion.py
@@ -0,0 +1,116 @@
+import os
+import sys
+p = os.path.split(os.path.dirname(os.path.abspath(__file__)))[0]
+sys.path.append(p)
+import logging
+import argparse
+import pickle
+import numpy as np
+import tensorflow as tf
+from pprint import pformat
+import matplotlib.pyplot as plt
+import glob
+import tensorflow as tf
+
+from utils.hparams import HParams
+from models import get_model
+from datasets.speech import Dataset
+from sklearn.manifold import TSNE
+import seaborn as sns
+clrs = sns.color_palette("husl", 5)
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--cfg_file', type=str)
+parser.add_argument('--seed', type=int, default=1234)
+parser.add_argument('--gpu', type=str, default='0')
+args = parser.parse_args()
+params = HParams(args.cfg_file)
+# modify config
+params.mask_type = 'det_expand'
+
+os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
+#np.random.seed(args.seed)
+#tf.set_random_seed(args.seed)
+
+# data
+testset = Dataset("test", batch_size=100, set_size=200, mask_type=params.mask_type)
+testset.initialize()
+
+# model
+model = get_model(params)
+model.load()
+
+# run
+save_dir = f'{params.exp_dir}/evaluate/speech_expansion/'
+os.makedirs(save_dir, exist_ok=True)
+log_file = open(f'{save_dir}/log.txt', 'w')
+
+def evaluate(batch):
+    sample = model.execute(model.sample, batch)
+    return sample
+
+def visualize(input, mask, sample, save_path):
+    
+    #with open(f'{save_path}data.pkl', 'wb') as f:
+    #    pickle.dump((batch, sample), f)
+    sample = sample
+    mask = mask
+    input = input
+    expanded = input * mask + sample * (1 - mask)
+  
+    N = sample.shape[0]
+    D = sample.shape[1]
+    C = sample.shape[2]
+    if True:
+        cdict = {1: 'red', 2: 'blue', 3: 'green'}
+        for i in range(N):
+            expanded_embedded = TSNE(n_components=2, learning_rate=100, init='random', perplexity=60).fit_transform(expanded[i])
+ 
+            plt.figure(figsize=(9,9))
+            plt.tight_layout()
+            idx = np.where(mask[i,:,0]==0)
+            plt.scatter(expanded_embedded[idx, 0], expanded_embedded[idx,1], c = "#1A85FF", label="Synthesized", alpha=0.2)
+            idx = np.where(mask[i,:,0]==1)
+            plt.scatter(expanded_embedded[idx, 0], expanded_embedded[idx,1], c = "#D41159", label="Real")
+            plt.legend(fontsize="20", loc ="upper left")
+            plt.tick_params(left = False, right = False , labelleft = False ,
+                labelbottom = False, bottom = False)
+            plt.savefig(f"{save_path}embed_{i}.png", dpi=200)
+            plt.close()
+    if False:
+        D_id_color = {'0': u'orchid', '1': u'darkcyan', '2': u'grey', '3': u'dodgerblue', '4': u'turquoise', '5': u'darkviolet'}
+        sample = sample.reshape(N * D, C)
+        mask = mask.reshape(N * D, C)
+        input = input.reshape(N * D, C)
+        expanded = expanded.reshape(N * D, C)
+        expanded_embedded = TSNE(n_components=2, learning_rate=1, init='random', perplexity=50).fit_transform(expanded)
+        expanded_embedded = expanded_embedded.reshape(N, D, 2)
+        plt.figure(figsize=(10,10))
+        plt.tight_layout()
+        for i in range(N):
+            plt.scatter(expanded_embedded[i, :, 0], expanded_embedded[i, :,1], c = D_id_color[str(i % 6)])
+        plt.savefig(f"{save_path}embed.png", dpi=200)
+        plt.close()
+    
+    
+
+
+# test
+save_path = f'{save_dir}/test/'
+os.makedirs(save_path, exist_ok=True)
+samples = []
+inputs = []
+masks = []
+filenames = []
+num_sample_step = 20
+
+batch = testset.next_batch()
+for s in range(num_sample_step):
+    sample = evaluate(batch)
+    samples.append(sample)
+samples = np.concatenate(samples, axis=1)
+inputs = np.tile(batch['x'], (1, num_sample_step, 1))
+masks = np.tile(batch['b'], (1, num_sample_step, 1))
+visualize(inputs, masks, samples, save_path)
+log_file.close()
diff --git a/Phoneme_Hallucinator_v2/scripts/speech_expansion_ins.py b/Phoneme_Hallucinator_v2/scripts/speech_expansion_ins.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b5dd0eae5c7abcdda6fb22d360a74bdef7e735f
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/scripts/speech_expansion_ins.py
@@ -0,0 +1,160 @@
+import os
+import sys
+import time
+p = os.path.split(os.path.dirname(os.path.abspath(__file__)))[0]
+sys.path.append(p)
+import argparse
+import numpy as np
+import tensorflow as tf
+tf.compat.v1.disable_v2_behavior()
+
+from utils.hparams import HParams
+from models import get_model
+import torch
+
+
+set_size = 200
+threshold = 100
+
+
+def fast_cosine_dist(source_feats, matching_pool):
+    source_norms = torch.norm(source_feats, p=2, dim=-1)
+    matching_norms = torch.norm(matching_pool, p=2, dim=-1)
+    dotprod = -torch.cdist(source_feats[None], matching_pool[None], p=2)[0]**2 + source_norms[:, None]**2 + matching_norms[None]**2
+    dotprod /= 2
+
+    dists = 1 - ( dotprod / (source_norms[:, None] * matching_norms[None]) )
+    return dists
+
+
+def evaluate(batch, model):
+    sample = model.execute(model.sample, batch)
+    return sample
+
+
+def prematch(path, expanded):
+    uttrs_from_same_spk = sorted(list(path.parent.rglob('**/*.pt')))
+    uttrs_from_same_spk.remove(path)
+    candidates = []
+    for each in uttrs_from_same_spk:
+        candidates.append(torch.load(each))
+    candidates = torch.cat(candidates,0)
+    candidates = torch.cat([candidates, torch.tensor(expanded)], 0)
+    source_feats = torch.load(path)
+    source_feats=source_feats.to(torch.float32)
+    dists = fast_cosine_dist(source_feats.cpu(), candidates.cpu()).cpu()
+    best = dists.topk(k=args.topk, dim=-1, largest=False) # (src_len, 4)
+    out_feats = candidates[best.indices].mean(dim=1) # (N, dim)
+    return out_feats
+    
+
+def single_expand(path, model, num_samples, seed=1234, out_path=None):
+    np.random.seed(seed)
+    tf.compat.v1.set_random_seed(seed)
+    # test
+    matching_set = torch.load(path, map_location=torch.device('cpu')).numpy()
+    matching_set = matching_set / 10
+    matching_size = matching_set.shape[0]
+    new_samples = []
+    cur_num_samples = 0
+    while cur_num_samples < num_samples:
+        batch = dict()
+        if matching_size < threshold:
+            num_new_samples = set_size - matching_size
+            padded_data = np.zeros((num_new_samples, matching_set.shape[1]))
+            batch['b'] = np.concatenate([np.ones_like(matching_set), np.zeros_like(padded_data)], 0)[None, ...]
+            batch['x'] = np.concatenate([matching_set, padded_data], axis=0)[None, ...]
+            batch['m'] = np.ones_like(batch['b'])
+            sample = evaluate(batch, model)
+            new_sample = sample[0,matching_size:] * 10
+            cur_num_samples += num_new_samples
+        else:
+            num_new_samples = set_size - threshold
+            ind = np.random.choice(matching_size, threshold, replace=False)
+            padded_data = np.zeros((num_new_samples, matching_set.shape[1]))
+            obs_data = matching_set[ind]
+            batch['x'] = np.concatenate([obs_data, padded_data], 0)[None, ...]
+            batch['b'] = np.concatenate([np.ones_like(obs_data), np.zeros_like(padded_data)], 0)[None, ...]
+            batch['m'] = np.ones_like(batch['b'])
+            sample = evaluate(batch, model)
+            new_sample = sample[0,num_new_samples:,:] * 10
+            cur_num_samples += num_new_samples
+        
+        new_samples.append(new_sample)
+    new_samples = np.concatenate(new_samples, 0)
+    new_samples = new_samples[:num_samples]
+    if out_path:
+        os.makedirs(os.path.dirname(out_path), exist_ok=True)
+        np.save(out_path, new_samples)
+    return new_samples
+
+
+def single_expand_fast(path):
+    # test
+    matching_set = torch.load(path).cpu().numpy()
+    matching_set = matching_set / 10
+    matching_size = matching_set.shape[0]
+    batch = dict()
+    if matching_size < threshold:
+        num_new_samples = set_size - matching_size
+    else:
+        num_new_samples = set_size - threshold
+    batch_size = int(np.ceil(args.num_samples // num_new_samples))
+    if matching_size < threshold:
+        padded_data = np.zeros((num_new_samples, matching_set.shape[1]))
+        batch['b'] = np.concatenate([np.ones_like(matching_set), np.zeros_like(padded_data)], 0)[None, ...]
+        batch['x'] = np.concatenate([matching_set, padded_data], axis=0)[None, ...]
+        batch['b'] = np.tile(batch['b'], (batch_size, 1, 1))
+        batch['x'] = np.tile(batch['b'], (batch_size, 1, 1))
+        batch['m'] = np.ones_like(batch['b'])
+        sample = evaluate(batch, model)
+        new_samples = sample[:,matching_size:, :] * 10
+        new_samples = new_samples.reshape((-1, new_samples.shape[-1]))
+    else:
+        padded_data = np.zeros((num_new_samples, matching_set.shape[1]))
+        batch['x'] = []
+        for i in range(batch_size):
+            ind = np.random.choice(matching_size, threshold, replace=False)
+            obs_data = matching_set[ind]
+            batch['x'].append(np.concatenate([obs_data, padded_data], 0)[None, ...])
+        batch['x'] = np.concatenate(batch['x'], 0)
+        batch['b'] = np.concatenate([np.ones_like(obs_data), np.zeros_like(padded_data)], 0)[None, ...]
+        batch['b'] = np.tile(batch['b'], (batch_size, 1, 1))
+        batch['m'] = np.ones_like(batch['b'])
+        sample = evaluate(batch, model)
+        new_samples = sample[:,matching_size:, :] * 10
+        new_samples = new_samples.reshape((-1, new_samples.shape[-1]))
+    new_samples = new_samples[:args.num_samples]
+    return new_samples
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--cfg_file', type=str)
+    parser.add_argument('--seed', type=int, default=1234)
+    parser.add_argument('--gpu', type=str, default='0')
+    parser.add_argument('--num_samples', type=int, default=100)
+    parser.add_argument('--path', type=str, default="matching_set.pt")
+    parser.add_argument('--out_path', type=str, default="expanded_set.pt")
+    parser.add_argument('--topk', type=int, default=4)
+    args = parser.parse_args()
+    params = HParams(args.cfg_file)
+    # modify config
+
+    t0 = time.time()
+    # model
+    model = get_model(params)
+    model.load()
+    t1 = time.time()
+    print(f"{t1-t0:.2f}s to load the model")
+
+    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
+    path = args.path
+    if path.endswith(".pt"):
+        t0 = time.time()
+        expanded = single_expand(path, model, args.num_samples, args.seed, args.out_path)
+        t1 = time.time()
+        print(f"{t1-t0:.2f}s to expand the set")
+            
+
+
+
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diff --git a/Phoneme_Hallucinator_v2/utils/hparams.py b/Phoneme_Hallucinator_v2/utils/hparams.py
new file mode 100644
index 0000000000000000000000000000000000000000..b79a9284a21880963c4099a172ed989d14d67e18
--- /dev/null
+++ b/Phoneme_Hallucinator_v2/utils/hparams.py
@@ -0,0 +1,25 @@
+import json
+
+
+class HParams():
+    def __init__(self, json_path):
+        self.load(json_path)
+
+    def save(self, json_path):
+        """Saves parameters to json file"""
+        with open(json_path, 'w') as f:
+            json.dump(self.__dict__, f, indent=4)
+
+    def load(self, json_path):
+        """Loads parameters from json file"""
+        with open(json_path) as f:
+            params = json.load(f)
+            self.__dict__.update(params)
+
+    def update(self, params):
+        self.__dict__.update(params)
+
+    @property
+    def dict(self):
+        """Gives dict-like access to Params instance by `params.dict['learning_rate']`"""
+        return self.__dict__
diff --git a/SVCNN.py b/SVCNN.py
new file mode 100644
index 0000000000000000000000000000000000000000..1421aae28fd67ddacfdc16b75428e8678d5accbc
--- /dev/null
+++ b/SVCNN.py
@@ -0,0 +1,206 @@
+import os
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchaudio
+import torchaudio.transforms as T
+from models import GeneratorNSF as Generator, PitchEncoder as PitchEmbedding
+from torch import Tensor
+from torchaudio.sox_effects import apply_effects_tensor
+from wavlm.WavLM import WavLM, WavLMConfig
+import json
+from utils.tools import AttrDict, load_wav
+
+
+SPEAKER_INFORMATION_WEIGHTS = [
+    0, 0, 0, 0, 0, 0,  # layer 0-5
+    1.0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, # layer 15
+    0, 0, 0, 0, 0, 0, # layer 16-21
+    0, # layer 22 
+    0, 0 # layer 23-24
+]
+SPEAKER_INFORMATION_LAYER = 6
+
+
+def fast_cosine_dist(source_feats: Tensor, matching_pool: Tensor, device: str = 'cpu') -> Tensor:
+    """ Like torch.cdist, but fixed dim=-1 and for cosine distance."""
+    source_norms = torch.norm(source_feats, p=2, dim=-1).to(device)
+    matching_norms = torch.norm(matching_pool, p=2, dim=-1)
+    dotprod = -torch.cdist(source_feats[None].to(device), matching_pool[None], p=2)[0]**2 + source_norms[:, None]**2 + matching_norms[None]**2
+    dotprod /= 2
+
+    dists = 1 - ( dotprod / (source_norms[:, None] * matching_norms[None]) )
+    return dists
+
+
+class SVCNN(nn.Module):
+    def __init__(self,
+        model_ckpt_path,
+        model_cfg_path='config.json',
+        wavlm_ckpt_path='ckpt/WavLM-Large.pt',
+        device='cpu'
+    ) -> None:
+        super().__init__()
+        # set which features to extract from wavlm
+        self.weighting = torch.tensor(SPEAKER_INFORMATION_WEIGHTS, device=device)[:, None]
+        # load model
+        with open(model_cfg_path) as f:
+            data = f.read()
+        json_config = json.loads(data)
+        model_cfg = AttrDict(json_config)
+        pitch_emb = PitchEmbedding(model_cfg).to(device)
+        model = Generator(model_cfg).to(device)
+        state_dict_g = torch.load(model_ckpt_path, map_location='cpu')
+        pitch_emb.load_state_dict(state_dict_g['pitch_encoder'])
+        model.load_state_dict(state_dict_g['generator'])
+        model.remove_weight_norm()
+        self.pitch_emb = pitch_emb.to(device).eval()
+        self.model = model.to(device).eval()
+        print(f"Generator loaded with {sum([p.numel() for p in model.parameters() if p.requires_grad]) + sum([p.numel() for p in pitch_emb.parameters() if p.requires_grad]):,d} parameters.")
+        self.h = model_cfg
+        # load wavlm
+        wavlm_ckpt = torch.load(wavlm_ckpt_path, map_location='cpu')
+        cfg = WavLMConfig(wavlm_ckpt['cfg'])
+        wavlm = WavLM(cfg)
+        wavlm.load_state_dict(wavlm_ckpt['model'])
+        wavlm.to(device)
+        self.wavlm = wavlm.eval()
+        print('wavlm loaded')
+        self.device = torch.device(device)
+        self.sr = 16000
+        self.hop_length = 320
+
+    def get_matching_set(self, p: Path|str, weights=None, vad_trigger_level=0, out_path=None) -> Tensor:
+        """ Get concatenated wavlm features for the matching set using all waveforms in `wavs`, 
+        specified as either a list of paths or list of loaded waveform tensors of 
+        shape (channels, T), assumed to be of 16kHz sample rate.
+        Optionally specify custom WavLM feature weighting with `weights`.
+        """
+        feats = []
+        # 只取有声段作为matching_set
+
+        # x, sr = torchaudio.load(p, normalize=True)
+        x, sr = load_wav(p, self.sr)
+        # x, _, __ = trim_long_silences(x, sr)
+        audio_length = len(x)
+        slice_length = 60*self.sr
+
+        for start_pos in range(0, audio_length, slice_length):
+            end_pos = start_pos + slice_length
+            slice_x = x[start_pos:end_pos]
+            slice_x = torch.from_numpy(slice_x).float()
+            feats.append(self.get_features(slice_x, weights=self.weighting if weights is None else weights, vad_trigger_level=vad_trigger_level))
+            
+        feats = torch.concat(feats, dim=0).cpu()
+        if out_path:
+            os.makedirs(os.path.dirname(out_path), exist_ok=True)
+            torch.save(feats, out_path)
+        return feats
+        
+
+    @torch.inference_mode()
+    def vocode(self, c: Tensor, pitch:Tensor) -> Tensor:
+        y_g_hat = self.model(c, pitch)
+        y_g_hat = y_g_hat.squeeze(1)
+        return y_g_hat
+
+
+    @torch.inference_mode()
+    def get_features(self, path, weights=None, vad_trigger_level=0):
+        """Returns features of `path` waveform as a tensor of shape (seq_len, dim), optionally perform VAD trimming
+        on start/end with `vad_trigger_level`.
+        """
+        # load audio
+        if weights == None: weights = self.weighting
+        if type(path) in [str, Path]:
+            # x, sr = torchaudio.load(path, normalize=True)
+            x, sr = load_wav(path, self.sr)
+            x = torch.from_numpy(x).float()
+        else:
+            x: Tensor = path
+            sr = self.sr
+        if x.dim() == 1: x = x[None]
+        assert sr == self.sr, f"input audio sample rate must be 16kHz. Got {sr}"
+        
+        # trim silence from front and back
+        if vad_trigger_level > 1e-3:
+            transform = T.Vad(sample_rate=sr, trigger_level=vad_trigger_level)
+            x_front_trim = transform(x)
+            waveform_reversed, sr = apply_effects_tensor(x_front_trim, sr, [["reverse"]])
+            waveform_reversed_front_trim = transform(waveform_reversed)
+            waveform_end_trim, sr = apply_effects_tensor(
+                waveform_reversed_front_trim, sr, [["reverse"]]
+            )
+            x = waveform_end_trim
+
+        # extract the representation of each layer
+        wavs_split = torch.tensor_split(x, (x.shape[1]-1)//(sr*30)+1, dim=1)
+        feature_list = []
+        for wav_chunk in wavs_split:
+            wav_input_16khz = wav_chunk.to(self.device)
+            if torch.allclose(weights, self.weighting):
+                # use fastpath
+                features = self.wavlm.extract_features(wav_input_16khz, output_layer=SPEAKER_INFORMATION_LAYER, ret_layer_results=False)[0]
+                features = features.squeeze(0)
+                feature_list.append(features)
+            else:
+                # use slower weighted
+                rep, layer_results = self.wavlm.extract_features(wav_input_16khz, output_layer=self.wavlm.cfg.encoder_layers, ret_layer_results=True)[0]
+                features = torch.cat([x.transpose(0, 1) for x, _ in layer_results], dim=0) # (n_layers, seq_len, dim)
+                # save full sequence
+                features = (features*weights[:, None] ).sum(dim=0) # (seq_len, dim)
+                feature_list.append(features)
+        
+        return torch.cat(feature_list)
+
+
+    @torch.inference_mode()
+    def match(self, query_seq: Tensor, pitch:Tensor, pitch_bins:Tensor, synth_set: Tensor,
+              topk: int = 4, query_mask: Tensor = None, alpha = 0, tgt_loudness_db: float | None = -16,
+              target_duration: float | None = None, device: str | None = None) -> Tensor:
+        """ Given `query_seq`, `matching_set`, and `synth_set` tensors of shape (N, dim), perform kNN regression matching
+        with k=`topk`. Inputs:
+            - `query_seq`: Tensor (N1, dim) of the input/source query features.
+            - `matching_set`: Tensor (N2, dim) of the matching set used as the 'training set' for the kNN algorithm.
+            - `synth_set`: optional Tensor (N2, dim) corresponding to the matching set. We use the matching set to assign each query
+                vector to a vector in the matching set, and then use the corresponding vector from the synth set during decoder synthesis.
+                By default, and for best performance, this should be identical to the matching set. 
+            - `topk`: k in the kNN -- the number of nearest neighbors to average over.
+            - `tgt_loudness_db`: float db used to normalize the output volume. Set to None to disable. 
+            - `target_duration`: if set to a float, interpolate resulting waveform duration to be equal to this value in seconds.
+            - `device`: if None, uses default device at initialization. Otherwise uses specified device
+        Returns:
+            - converted waveform of shape (T,)
+        """
+        device = torch.device(device) if device is not None else self.device
+        synth_set = synth_set.to(device)
+        query_seq = query_seq.to(device)
+        pitch = pitch.to(device)
+        pitch_bins = pitch_bins.to(device)
+
+        if target_duration is not None:
+            target_samples = int(target_duration*self.sr)
+            scale_factor = (target_samples/self.hop_length) / query_seq.shape[0] # n_targ_feats / n_input_feats
+            query_seq = F.interpolate(query_seq.T[None], scale_factor=scale_factor, mode='linear')[0].T
+
+        dists = fast_cosine_dist(query_seq, synth_set, device=device)
+        best = dists.topk(k=topk, largest=False, dim=-1)
+        out_feats = (1-alpha) * synth_set[best.indices].mean(dim=1) + alpha *  query_seq
+        if query_mask is not None:
+            query_mask = query_mask[..., None].repeat([1, out_feats.shape[-1]])
+            out_feats = out_feats * query_mask + query_seq * (query_mask == False)
+        out_feats = torch.repeat_interleave(out_feats, 2, 0)
+        out_feats = self.pitch_emb(out_feats, pitch_bins)
+        
+        prediction = self.vocode(out_feats[None].to(device), pitch.unsqueeze(0)).cpu().squeeze()
+        
+        # normalization
+        if tgt_loudness_db is not None:
+            src_loudness = torchaudio.functional.loudness(prediction[None], self.h.sampling_rate)
+            tgt_loudness = tgt_loudness_db
+            pred_wav = torchaudio.functional.gain(prediction, tgt_loudness - src_loudness)
+        else: pred_wav = prediction
+        return pred_wav
\ No newline at end of file
diff --git a/ckpt/G_150k.pt b/ckpt/G_150k.pt
new file mode 100644
index 0000000000000000000000000000000000000000..970166195d3c32c277053ff1ad9a705260ee35ec
--- /dev/null
+++ b/ckpt/G_150k.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e7b1a67aef7d681ba7a85f60e5760e006f1fc4fd834e001f16aad5f3188f98b1
+size 66274814
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..3f21fd0eb0a92f66daf34d5606b34b1838ec22d6
--- /dev/null
+++ b/config.json
@@ -0,0 +1,40 @@
+{
+    "resblock": "1",
+    "num_gpus": 0,
+    "batch_size": 256,
+    "learning_rate": 0.0002,
+    "adam_b1": 0.8,
+    "adam_b2": 0.99,
+    "lr_decay": 0.999,
+    "seed": 1234,
+
+    "upsample_rates": [10,6,2,2],
+    "upsample_kernel_sizes": [20,12,4,4],
+    "upsample_initial_channel": 512,
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+
+    "hubert_dim": 1024,
+    "hifi_dim": 512,
+
+    "segment_size": 7680,
+    "num_mels": 80,
+    "num_freq": 1025,
+    "n_fft": 1024,
+    "hop_size": 240,
+    "win_size": 1024,
+
+    "sampling_rate": 24000,
+
+    "fmin": 0,
+    "fmax": 8000,
+    "fmax_for_loss": null,
+
+    "num_workers": 4,
+
+    "dist_config": {
+        "dist_backend": "nccl",
+        "dist_url": "tcp://localhost:54321",
+        "world_size": 1
+    }
+}
diff --git a/inference.py b/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..d328abb42f532421340e2d5e79d6c961f7aadc48
--- /dev/null
+++ b/inference.py
@@ -0,0 +1,126 @@
+import os
+import time
+
+import numpy as np
+import torch
+import soundfile as sf
+import argparse
+
+from SVCNN import SVCNN
+from utils.tools import extract_voiced_area
+from utils.extract_pitch import extract_pitch_ref as extract_pitch, coarse_f0
+
+SPEAKER_INFORMATION_WEIGHTS = [
+    0, 0, 0, 0, 0, 0,  # layer 0-5
+    1.0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0,  # layer 15
+    0, 0, 0, 0, 0, 0,  # layer 16-21
+    0,  # layer 22
+    0, 0  # layer 23-24
+]
+SPEAKER_INFORMATION_LAYER = 6
+
+
+APPLIED_INFORMATION_WEIGHTS = [
+    0, 0, 0, 0, 0, 0,  # layer 0-5
+    0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0,  # layer 15
+    0, 0, 0, 0, 0.2, 0.2,  # layer 16-21
+    0.2,  # layer 22
+    0.2, 0.2  # layer 23-24
+]
+
+
+def svc(model, src_wav_path, ref_wav_path, out_dir, device, f0_factor, speech_enroll=False):
+    
+    wav_name = os.path.basename(src_wav_path).split('.')[0]
+    ref_name = os.path.basename(ref_wav_path).split('.')[0]
+
+    f0_src, f0_factor = extract_pitch(src_wav_path, ref_wav_path, predefined_factor=f0_factor, speech_enroll=speech_enroll)
+
+    pitch_src = coarse_f0(f0_src)
+
+    query_mask = extract_voiced_area(src_wav_path, hop_size=480, energy_thres=0.1)
+    query_mask = torch.from_numpy(query_mask).to(device)
+
+    synth_weights = torch.tensor(
+        SPEAKER_INFORMATION_WEIGHTS, device=device)[:, None]
+    query_seq = model.get_features(
+        src_wav_path, weights=synth_weights)
+
+    synth_set_path = f"matching_set/{ref_name}.pt"
+    synth_set = model.get_matching_set(ref_wav_path, out_path=synth_set_path).to(device)
+    hallucinated_set_path = f"matching_set/hallucinated_set/{ref_name}_hallucinated_15k.npy"
+    os.system(f"python Phoneme_Hallucinator_v2/scripts/speech_expansion_ins.py --cfg_file Phoneme_Hallucinator_v2/exp/speech_XXL_cond/params.json --num_samples 15000 --path {synth_set_path} --out_path {hallucinated_set_path}")
+    hallucinated_set = torch.from_numpy(np.load(hallucinated_set_path)).to(device)
+    synth_set = torch.cat([synth_set, hallucinated_set], dim=0)
+    
+    query_len = query_seq.shape[0]
+    if len(query_mask) > query_len:
+        query_mask = query_mask[:query_len]
+    else:
+        p = query_len - len(query_mask)
+        query_mask = np.pad(query_mask, (0, p))
+
+    f0_len = query_len*2
+    if len(f0_src) > f0_len:
+        f0_src = f0_src[:f0_len]
+        pitch_src = pitch_src[:f0_len]
+    else:
+        p = f0_len-len(f0_src)
+        f0_src = np.pad(f0_src, (0, p), mode='edge')
+        pitch_src = np.pad(pitch_src, (0, p), mode='edge')
+    
+    print(query_seq.shape)
+    print(synth_set.shape)
+
+    f0_src = torch.from_numpy(f0_src).float().to(device)
+    pitch_src = torch.from_numpy(pitch_src).to(device)
+
+    out_wav = model.match(query_seq, f0_src, pitch_src, synth_set, topk=4, query_mask=query_mask)
+    # out_wav is (T,) tensor converted 16kHz output wav using k=4 for kNN.
+    os.makedirs(out_dir, exist_ok=True)
+    wfname = f'{out_dir}/{wav_name}_{ref_name}_{f0_factor:.2f}_NeuCoSVCv2.wav'
+
+    sf.write(wfname, out_wav.numpy(), 24000)
+
+
+def main(a):
+    model_ckpt_path = a.model_ckpt_path
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    # device = 'cpu'
+    print(f'using {device} for inference')
+
+    f0factor = pow(2, a.key_shift / 12) if a.key_shift else 0.
+
+    speech_enroll = a.speech_enroll
+    model = SVCNN(model_ckpt_path, device=device)
+
+    t0 = time.time()
+    svc(model, a.src_wav_path, a.ref_wav_path, a.out_dir, device, f0factor, speech_enroll)
+    t1 = time.time()
+    print(f"{t1-t0:.2f}s to perfrom the conversion")
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--src_wav_path', required=True)
+    parser.add_argument('--ref_wav_path', required=True)
+    parser.add_argument('--model_ckpt_path',
+                        default='ckpt/G_150k.pt')
+    parser.add_argument('--out_dir', default='output')
+    parser.add_argument(
+        '--key_shift', type=int,
+        help='Adjust the pitch of the source singing. Tone the song up or down in semitones.'
+    )
+    parser.add_argument(
+        '--speech_enroll', action='store_true',
+        help='When using speech as the reference audio, the pitch of the reference audio will be increased by 1.2 times \
+            when performing pitch shift to cover the pitch gap between singing and speech. \
+            Note: This option is invalid when key_shift is specified.'
+    )
+
+    a = parser.parse_args()
+
+    main(a)
diff --git a/models.py b/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d13ba64eba3aa95fdda5a0b635fa9e04b06b2e4
--- /dev/null
+++ b/models.py
@@ -0,0 +1,434 @@
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from utils.tools import init_weights, get_padding
+import numpy as np
+
+LRELU_SLOPE = 0.1
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
+                               padding=get_padding(kernel_size, dilation[2])))
+        ])
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1)))
+        ])
+        self.convs2.apply(init_weights)
+
+    def forward(self, x):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            xt = c2(xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1])))
+        ])
+        self.convs.apply(init_weights)
+
+    def forward(self, x):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c(xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class PitchEncoder(torch.nn.Module):
+    def __init__(self, h):
+        super(PitchEncoder, self).__init__()
+        self.lin_pre = nn.Linear(h.hubert_dim, h.hifi_dim)
+        self.pitch_emb = nn.Embedding(256, h.hifi_dim)
+    
+    def forward(self, x, pitch):
+        x = self.lin_pre(x) + self.pitch_emb(pitch)
+        return x
+
+
+class SineGen(torch.nn.Module):
+    """ Definition of sine generator
+    SineGen(samp_rate, harmonic_num = 0, 
+            sine_amp = 0.1, noise_std = 0.003,
+            voiced_threshold = 0,
+            flag_for_pulse=False)
+    
+    samp_rate: sampling rate in Hz
+    harmonic_num: number of harmonic overtones (default 0)
+    sine_amp: amplitude of sine-wavefrom (default 0.1)
+    noise_std: std of Gaussian noise (default 0.003)
+    voiced_thoreshold: F0 threshold for U/V classification (default 0)
+    flag_for_pulse: this SinGen is used inside PulseGen (default False)
+    
+    Note: when flag_for_pulse is True, the first time step of a voiced
+        segment is always sin(np.pi) or cos(0)
+    """
+    def __init__(self, samp_rate, harmonic_num = 0, 
+                 sine_amp = 0.1, noise_std = 0.003,
+                 voiced_threshold = 0,
+                 flag_for_pulse=False):
+        super(SineGen, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.dim = self.harmonic_num + 1
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+        self.flag_for_pulse = flag_for_pulse
+    
+    def _f02uv(self, f0):
+        # generate uv signal
+        uv = torch.ones_like(f0)
+        uv = uv * (f0 > self.voiced_threshold)
+        return uv
+            
+    def _f02sine(self, f0_values, upp):
+        """ f0_values: (batchsize, length, dim)
+            where dim indicates fundamental tone and overtones
+        """
+        # convert to F0 in rad. The interger part n can be ignored
+        # because 2 * np.pi * n doesn't affect phase
+        rad_values = (f0_values / self.sampling_rate) % 1
+        
+        # initial phase noise (no noise for fundamental component)
+        rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2],\
+                              device = f0_values.device)
+        rand_ini[:, 0] = 0
+        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini        
+        
+        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
+        # for normal case
+
+        # To prevent torch.cumsum numerical overflow,
+        # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
+        # Buffer tmp_over_one_idx indicates the time step to add -1.
+        # This will not change F0 of sine because (x-1) * 2*pi = x *2*pi
+        tmp_over_one = torch.cumsum(rad_values, 1) % 1
+        tmp_over_one *= upp
+        tmp_over_one = F.interpolate(
+            tmp_over_one.transpose(2, 1), scale_factor=upp,
+            mode='linear', align_corners=True
+        ).transpose(2, 1)
+        rad_values = F.interpolate(rad_values.transpose(2, 1), scale_factor=upp, mode='nearest').transpose(2, 1)
+        tmp_over_one %= 1
+        tmp_over_one_idx = (tmp_over_one[:, 1:, :] - 
+                            tmp_over_one[:, :-1, :]) < 0
+        cumsum_shift = torch.zeros_like(rad_values)
+        cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+        
+        sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) \
+                            * 2 * np.pi)
+        return  sines
+    
+    
+    def forward(self, f0, upp):
+        """ sine_tensor, uv = forward(f0)
+        input F0: tensor(batchsize=1, length, dim=1)
+                  f0 for unvoiced steps should be 0
+        output sine_tensor: tensor(batchsize=1, length, dim)
+        output uv: tensor(batchsize=1, length, 1)
+        """
+        with torch.no_grad():
+            f0 = f0.unsqueeze(-1)
+            f0_buf = torch.multiply(f0, torch.arange(1, self.dim + 1, device=f0.device).reshape((1, 1, -1)))
+    
+            # generate sine waveforms
+            sine_waves = self._f02sine(f0_buf, upp) * self.sine_amp
+            
+            # generate uv signal
+            #uv = torch.ones(f0.shape)
+            #uv = uv * (f0 > self.voiced_threshold)
+            uv = self._f02uv(f0)
+            uv = F.interpolate(uv.transpose(2, 1), scale_factor=upp, mode='nearest').transpose(2, 1)
+        
+            # noise: for unvoiced should be similar to sine_amp
+            #        std = self.sine_amp/3 -> max value ~ self.sine_amp
+            #.       for voiced regions is self.noise_std
+            noise_amp = uv * self.noise_std + (1-uv) * self.sine_amp / 3
+            noise = noise_amp * torch.randn_like(sine_waves)
+            
+            # first: set the unvoiced part to 0 by uv
+            # then: additive noise
+            sine_waves = sine_waves * uv + noise
+        return sine_waves, uv, noise
+
+
+class SourceModuleHnNSF(torch.nn.Module):
+    """ SourceModule for hn-nsf 
+    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, 
+                 add_noise_std=0.003, voiced_threshod=0)
+    sampling_rate: sampling_rate in Hz
+    harmonic_num: number of harmonic above F0 (default: 0)
+    sine_amp: amplitude of sine source signal (default: 0.1)
+    add_noise_std: std of additive Gaussian noise (default: 0.003)
+        note that amplitude of noise in unvoiced is decided
+        by sine_amp
+    voiced_threshold: threhold to set U/V given F0 (default: 0)
+
+    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+    F0_sampled (batchsize, length, 1)
+    Sine_source (batchsize, length, 1)
+    noise_source (batchsize, length 1)
+    uv (batchsize, length, 1)
+    """
+    def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1, 
+                 add_noise_std=0.003, voiced_threshod=0):
+        super(SourceModuleHnNSF, self).__init__()
+        
+        self.sine_amp = sine_amp
+        self.noise_std = add_noise_std
+
+        # to produce sine waveforms
+        self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
+                                 sine_amp, add_noise_std, voiced_threshod)
+
+        # to merge source harmonics into a single excitation
+        self.l_linear = torch.nn.Linear(harmonic_num+1, 1)
+        self.l_tanh = torch.nn.Tanh()
+
+    def forward(self, x, upp):
+        """
+        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+        F0_sampled (batchsize, length, 1)
+        Sine_source (batchsize, length, 1)
+        noise_source (batchsize, length 1)
+        """
+        # source for harmonic branch
+        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
+        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+
+        # source for noise branch, in the same shape as uv
+        # noise = torch.randn_like(uv) * self.sine_amp / 3
+        return sine_merge
+
+
+class GeneratorNSF(torch.nn.Module):
+    def __init__(self, h):
+        super(GeneratorNSF, self).__init__()
+        self.num_kernels = len(h.resblock_kernel_sizes)
+        self.num_upsamples = len(h.upsample_rates)
+        self.m_source = SourceModuleHnNSF(
+            sampling_rate=h.sampling_rate,
+            harmonic_num=8
+        )
+        self.noise_convs = nn.ModuleList()
+
+        self.conv_pre = weight_norm(Conv1d(h.hifi_dim, h.upsample_initial_channel, 7, 1, padding=3))
+        resblock = ResBlock1 if h.resblock == '1' else ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
+
+            c_cur = h.upsample_initial_channel // (2 ** (i + 1))
+            self.ups.append(weight_norm(
+                ConvTranspose1d(h.upsample_initial_channel//(2**i), h.upsample_initial_channel//(2**(i+1)),
+                                k, u, padding=(k-u)//2)))
+            if i + 1 < len(h.upsample_rates):  #
+                stride_f0 = int(np.prod(h.upsample_rates[i + 1:]))
+                self.noise_convs.append(Conv1d(
+                    1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
+            else:
+                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = h.upsample_initial_channel//(2**(i+1))
+            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
+                self.resblocks.append(resblock(h, ch, k, d))
+
+        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
+        self.ups.apply(init_weights)
+        self.conv_post.apply(init_weights)
+        self.upp = int(np.prod(h.upsample_rates))
+
+    def forward(self, x, f0):
+        """ `x` as (bs, seq_len, dim), regular hifi assumes input of shape (bs, n_mels, seq_len) """
+        x = x.permute(0, 2, 1) # (bs, seq_len, dim) --> (bs, dim, seq_len)
+        har_source = self.m_source(f0, self.upp).transpose(1, 2)
+
+        x = self.conv_pre(x)
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            x = self.ups[i](x)
+            x_source = self.noise_convs[i](har_source)
+            x = x + x_source
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i*self.num_kernels+j](x)
+                else:
+                    xs += self.resblocks[i*self.num_kernels+j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+        remove_weight_norm(self.conv_pre)
+        remove_weight_norm(self.conv_post)
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
+        ])
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0: # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self):
+        super(MultiPeriodDiscriminator, self).__init__()
+        self.discriminators = nn.ModuleList([
+            DiscriminatorP(2),
+            DiscriminatorP(3),
+            DiscriminatorP(5),
+            DiscriminatorP(7),
+            DiscriminatorP(11),
+        ])
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            fmap_rs.append(fmap_r)
+            y_d_gs.append(y_d_g)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv1d(1, 128, 15, 1, padding=7)),
+            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
+            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
+            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
+            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+        ])
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiScaleDiscriminator(torch.nn.Module):
+    def __init__(self):
+        super(MultiScaleDiscriminator, self).__init__()
+        self.discriminators = nn.ModuleList([
+            DiscriminatorS(use_spectral_norm=True),
+            DiscriminatorS(),
+            DiscriminatorS(),
+        ])
+        self.meanpools = nn.ModuleList([
+            AvgPool1d(4, 2, padding=2),
+            AvgPool1d(4, 2, padding=2)
+        ])
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            if i != 0:
+                y = self.meanpools[i-1](y)
+                y_hat = self.meanpools[i-1](y_hat)
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            fmap_rs.append(fmap_r)
+            y_d_gs.append(y_d_g)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
diff --git a/requirements b/requirements
new file mode 100644
index 0000000000000000000000000000000000000000..b13ee1796ffc092257d2e9991f80138e36bd3b50
--- /dev/null
+++ b/requirements
@@ -0,0 +1,11 @@
+easydict
+librosa
+numpy
+praat-parselmouth
+resampy
+soundfile
+tensorflow==2.15.0
+tensorflow-probability==0.23.0
+tf-keras==2.15.0
+torch==2.0.1
+torchaudio==2.0.2
diff --git a/utils/__pycache__/extract_pitch.cpython-310.pyc b/utils/__pycache__/extract_pitch.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7d237c1d5cc561f00b521c4a782608d1184eaa82
Binary files /dev/null and b/utils/__pycache__/extract_pitch.cpython-310.pyc differ
diff --git a/utils/__pycache__/spectrogram.cpython-310.pyc b/utils/__pycache__/spectrogram.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d797c5527ea8f94da2c3cba24a6784ac37892f93
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diff --git a/utils/__pycache__/tools.cpython-310.pyc b/utils/__pycache__/tools.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..26efa63d005ad2b6c4b20fcf88b985a6b3963312
Binary files /dev/null and b/utils/__pycache__/tools.cpython-310.pyc differ
diff --git a/utils/extract_pitch.py b/utils/extract_pitch.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbe9a81fa1654ec397ba9ef1280ce31b70d75e7c
--- /dev/null
+++ b/utils/extract_pitch.py
@@ -0,0 +1,195 @@
+import numpy as np
+import os
+import random
+import librosa
+import parselmouth
+
+from utils.tools import load_wav
+
+np.random.seed(0)
+random.seed(0)
+
+
+def REAPER_F0(wav_path, sr=24000, frame_period=0.01):  # frame_period s
+    if not os.path.isfile(f'{wav_path}.f0'):
+        cmd = f'/group/40034/xuli/SVC/REAPER-master/build/reaper -i {wav_path} -f {wav_path}.f0 -e {frame_period} -x 1000 -m 65 -a'
+        os.system(cmd)
+    f0 = []
+    try:
+        with open(f'{wav_path}.f0', 'r') as rf:
+            for line in rf.readlines()[7:]:
+                f0.append(float(line.split()[2]))
+    except FileNotFoundError as e:
+        return None
+
+    cmd = f'rm -f {wav_path}.f0'
+    os.system(cmd)
+
+    f0 = np.array(f0)
+    minus_one_indexes = (f0 == -1)
+    f0[minus_one_indexes] = 0
+
+    return f0
+
+
+def ParselMouth_F0(wav, sr=24000, frame_period=0.01):
+    wav = parselmouth.Sound(wav, sampling_frequency=sr)
+    pitch = wav.to_pitch(time_step=frame_period, pitch_floor=65, pitch_ceiling=1000)
+    f0 = pitch.selected_array['frequency']
+
+    return f0
+
+
+def PYIN_F0(wav, sr=24000, frame_period=10):
+    fmin = librosa.note_to_hz('C2')  # ~65Hz
+    fmax = librosa.note_to_hz('C7')  # ~2093Hz
+    # fmax = fs/2
+    f0, voiced_flag, voiced_prob = librosa.pyin(
+        wav, fmin=fmin, fmax=fmax, sr=sr, frame_length=int(sr*frame_period/1000*4))
+    f0 = np.where(np.isnan(f0), 0.0, f0)
+    return f0
+
+
+def pad_arrays(arrays: list[np.ndarray], std_len: int):
+    """
+    Pad arrays value to a specified standard length.
+
+    Args:
+        arrays (List[numpy.ndarray]): List of arrays to be padded.
+        std_len (int): Standard length to which the arrays will be padded.
+
+    Returns:
+        List[numpy.ndarray]: List of padded arrays.
+
+    Raises:
+        ValueError: If the length of any array in the input list is greater than the specified standard length.
+    
+    """
+    padded_arrays = []
+    for arr in arrays:
+        cur_len = len(arr)
+        if cur_len <= std_len:
+            pad_width = std_len - cur_len
+            left_pad = pad_width // 2
+            right_pad = pad_width - left_pad
+            padded_arr = np.pad(arr, (left_pad, right_pad), 'edge')
+            padded_arrays.append(padded_arr)
+        else:
+            raise ValueError(f'cur_len: {cur_len}, std_len: {std_len}.')
+    return padded_arrays
+
+
+def compute_pitch(wav_path: str, pitch_path: str=None, frame_period=0.01):
+    """
+    Computes the pitch information from an audio waveform.
+
+    Args:
+        wav_path (str): Path to the audio waveform file (must be 24kHz).
+        pitch_path (str, optional): Path to save or load the computed pitch information as a numpy file. 
+            If specified, the function will first attempt to load the pitch information from this path. 
+            If the file does not exist, the pitch will be computed and saved to this path. 
+            Defaults to None.
+        frame_period (float, optional): Time duration in seconds for each frame. Defaults to 0.01.
+
+    Returns:
+        numpy.ndarray: Computed pitch information.
+
+    Notes:
+        For precise pitch representation, the pitch values are extracted by the median of three methods: 
+        the PYIN, the REAPER, and the Parselmouth.
+
+    """
+    import time
+    if pitch_path is not None and os.path.isfile(pitch_path):
+        pitch = np.load(pitch_path)
+        return pitch
+    else:
+        # extract pitch using 24kHz audio
+        wav, fs = load_wav(wav_path, 24000)
+        f0_std_len = wav.shape[0] // int(frame_period*fs) + 1
+
+        compute_median = []
+
+        # Compute pitch using PYIN algorithm
+        f0 = PYIN_F0(wav, sr=fs, frame_period=frame_period*1000)
+        compute_median.append(f0)
+
+        # Compute pitch using ParselMouth algorithm
+        f0 = ParselMouth_F0(wav, sr=fs, frame_period=frame_period)
+        compute_median.append(f0)
+
+        # Compute pitch using REAPER algorithm
+        f0 = REAPER_F0(wav_path, sr=fs, frame_period=frame_period)
+
+        if f0 is not None:
+            compute_median.append(f0)
+
+        # Compute median F0
+        compute_median = pad_arrays(compute_median, f0_std_len)
+        compute_median = np.array(compute_median)
+        median_f0 = np.median(compute_median, axis=0)
+        if pitch_path is not None:
+            os.makedirs(pitch_path.parent, exist_ok=True)
+            np.save(pitch_path, median_f0)
+        return median_f0
+
+
+def coarse_f0(f0):
+    f0_bin = 256
+    f0_max = 1000.0
+    f0_min = 65.0
+    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
+    f0_mel_max = 1127 * np.log(1 + f0_max / 700)
+    f0_mel = 1127 * np.log(1 + f0 / 700)
+    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (
+        f0_bin - 2
+    ) / (f0_mel_max - f0_mel_min) + 1
+
+    # use 0 or 1
+    f0_mel[f0_mel <= 1] = 1
+    f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
+    f0_coarse = np.rint(f0_mel).astype(int)
+    assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (
+        f0_coarse.max(),
+        f0_coarse.min(),
+    )
+    return f0_coarse
+
+
+def extract_pitch_ref(wav_path: str, ref_path: str, predefined_factor=0, speech_enroll=False):
+    """
+    Extracts pitch information from an audio waveform and adjusts it based on a reference audio.
+
+    Args:
+        wav_path (str): Path to the audio waveform file.
+        ref_path (str): Path to the reference audio waveform file.
+        predefined_factor (float, optional): Predefined factor to adjust the pitch. 
+            If non-zero, this factor will be used instead of computing it from the reference audio. Defaults to 0.
+        speech_enroll (bool, optional): Flag indicating whether the pitch adjustment is for speech enrollment. Defaults to False.
+
+    Returns:
+        Tuple[numpy.ndarray, float]: Tuple containing the adjusted pitch information (source_f0) and the pitch shift factor (factor).
+
+    """
+    source_f0 = compute_pitch(wav_path)
+    nonzero_indices = np.nonzero(source_f0)
+    source_mean = np.mean(source_f0[nonzero_indices], axis=0)
+
+    if predefined_factor != 0.:
+        print(f'Using predefined factor {predefined_factor}.')
+        factor = predefined_factor
+    else:
+        # Compute mean and std for pitch with the reference audio
+        ref_wav, fs = load_wav(ref_path)
+        ref_f0 = ParselMouth_F0(ref_wav, fs)
+        nonzero_indices = np.nonzero(ref_f0)
+        ref_mean = np.mean(ref_f0[nonzero_indices], axis=0)
+        factor = ref_mean / source_mean
+        if speech_enroll:
+            factor = factor * 1.2
+        print(f'pitch shift factor: {factor:.2f}')
+
+    # Modify f0 to fit with different persons
+    source_f0 = source_f0 * factor
+
+    return source_f0, factor
diff --git a/utils/spectrogram.py b/utils/spectrogram.py
new file mode 100644
index 0000000000000000000000000000000000000000..f643e163e2d351a2c53f9afd8093131ff481ed2e
--- /dev/null
+++ b/utils/spectrogram.py
@@ -0,0 +1,344 @@
+'''
+Copied from espnet: https://github.com/espnet/espnet/blob/master/espnet/transform/spectrogram.py
+'''
+import librosa
+import numpy as np
+
+# for sing + acc.
+def AWeightingLoudness_SingAcc(x, sr, n_fft, n_shift, win_length=None, window='hann', center=True, pad_mode='reflect'):
+    assert x.ndim == 1, 'The audio has %d channels, but so far we only support single-channel audios.' %(x.ndim)
+    # [Freq, Time]
+    mag_stft = np.abs(stft(x, n_fft, n_shift, win_length, window, center, pad_mode).T)
+
+    freq_axis = librosa.fft_frequencies(sr=sr, n_fft=n_fft)
+    perceptual_stft = librosa.perceptual_weighting(mag_stft**2, freq_axis, ref=1)
+    perceptual_loudness = np.log10(np.sum(np.power(10, perceptual_stft/10), axis=0)+1e-5)
+
+    return perceptual_loudness
+
+# Extract Log-Scaled A-Weighting Loudness from Audio. Added by Xu Li.
+def AWeightingLoudness(x, sr, n_fft, n_shift, win_length=None, window='hann', center=True, pad_mode='reflect'):
+    assert x.ndim == 1, 'The audio has %d channels, but so far we only support single-channel audios.' %(x.ndim)
+    # [Freq, Time]
+    mag_stft = np.abs(stft(x, n_fft, n_shift, win_length, window, center, pad_mode).T)
+
+    freq_axis = librosa.fft_frequencies(sr=sr, n_fft=n_fft)
+    perceptual_stft = librosa.perceptual_weighting(mag_stft**2, freq_axis, ref=1)
+    perceptual_loudness = np.log10(np.mean(np.power(10, perceptual_stft/10), axis=0)+1e-5)
+
+    return perceptual_loudness
+
+def VoicedAreaDetection(x, sr, n_fft, n_shift, win_length=None, window='hann', center=True, pad_mode='reflect', hi_freq=1000, energy_thres=0.5):
+    assert x.ndim == 1, 'The audio has %d channels, but so far we only support single-channel audios.' %(x.ndim)
+    # [Freq, Time]
+    mag_stft = np.abs(stft(x, n_fft, n_shift, win_length, window, center, pad_mode).T)
+    freq_axis = librosa.fft_frequencies(sr=sr, n_fft=n_fft)
+    filtered_mag_stft = mag_stft[freq_axis <= hi_freq]
+    loudness = np.log10(np.mean(np.power(10, filtered_mag_stft/10), axis=0)+1e-5)
+    
+    return loudness >= energy_thres
+
+
+def stft(
+    x, n_fft, n_shift, win_length=None, window="hann", center=True, pad_mode="reflect"
+):
+    # x: [Time, Channel]
+    if x.ndim == 1:
+        single_channel = True
+        # x: [Time] -> [Time, Channel]
+        x = x[:, None]
+    else:
+        single_channel = False
+    x = x.astype(np.float32)
+
+    # FIXME(kamo): librosa.stft can't use multi-channel?
+    # x: [Time, Channel, Freq]
+    x = np.stack(
+        [
+            librosa.stft(
+                x[:, ch],
+                n_fft=n_fft,
+                hop_length=n_shift,
+                win_length=win_length,
+                window=window,
+                center=center,
+                pad_mode=pad_mode,
+            ).T
+            for ch in range(x.shape[1])
+        ],
+        axis=1,
+    )
+
+    if single_channel:
+        # x: [Time, Channel, Freq] -> [Time, Freq]
+        x = x[:, 0]
+    return x
+
+
+def istft(x, n_shift, win_length=None, window="hann", center=True):
+    # x: [Time, Channel, Freq]
+    if x.ndim == 2:
+        single_channel = True
+        # x: [Time, Freq] -> [Time, Channel, Freq]
+        x = x[:, None, :]
+    else:
+        single_channel = False
+
+    # x: [Time, Channel]
+    x = np.stack(
+        [
+            librosa.istft(
+                x[:, ch].T,  # [Time, Freq] -> [Freq, Time]
+                hop_length=n_shift,
+                win_length=win_length,
+                window=window,
+                center=center,
+            )
+            for ch in range(x.shape[1])
+        ],
+        axis=1,
+    )
+
+    if single_channel:
+        # x: [Time, Channel] -> [Time]
+        x = x[:, 0]
+    return x
+
+
+def stft2logmelspectrogram(x_stft, fs, n_mels, n_fft, fmin=None, fmax=None, eps=1e-10):
+    # x_stft: (Time, Channel, Freq) or (Time, Freq)
+    fmin = 0 if fmin is None else fmin
+    fmax = fs / 2 if fmax is None else fmax
+
+    # spc: (Time, Channel, Freq) or (Time, Freq)
+    spc = np.abs(x_stft)
+    # mel_basis: (Mel_freq, Freq)
+    mel_basis = librosa.filters.mel(sr=fs, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
+    # lmspc: (Time, Channel, Mel_freq) or (Time, Mel_freq)
+    lmspc = np.log10(np.maximum(eps, np.dot(spc, mel_basis.T)))
+
+    return lmspc
+
+
+def spectrogram(x, n_fft, n_shift, win_length=None, window="hann"):
+    # x: (Time, Channel) -> spc: (Time, Channel, Freq)
+    spc = np.abs(stft(x, n_fft, n_shift, win_length, window=window))
+    return spc
+
+
+def logmelspectrogram(
+    x,
+    fs,
+    n_mels,
+    n_fft,
+    n_shift,
+    win_length=None,
+    window="hann",
+    fmin=None,
+    fmax=None,
+    eps=1e-10,
+    pad_mode="reflect",
+):
+    # stft: (Time, Channel, Freq) or (Time, Freq)
+    x_stft = stft(
+        x,
+        n_fft=n_fft,
+        n_shift=n_shift,
+        win_length=win_length,
+        window=window,
+        pad_mode=pad_mode,
+    )
+
+    return stft2logmelspectrogram(
+        x_stft, fs=fs, n_mels=n_mels, n_fft=n_fft, fmin=fmin, fmax=fmax, eps=eps
+    )
+
+
+class Spectrogram(object):
+    def __init__(self, n_fft, n_shift, win_length=None, window="hann"):
+        self.n_fft = n_fft
+        self.n_shift = n_shift
+        self.win_length = win_length
+        self.window = window
+
+    def __repr__(self):
+        return (
+            "{name}(n_fft={n_fft}, n_shift={n_shift}, "
+            "win_length={win_length}, window={window})".format(
+                name=self.__class__.__name__,
+                n_fft=self.n_fft,
+                n_shift=self.n_shift,
+                win_length=self.win_length,
+                window=self.window,
+            )
+        )
+
+    def __call__(self, x):
+        return spectrogram(
+            x,
+            n_fft=self.n_fft,
+            n_shift=self.n_shift,
+            win_length=self.win_length,
+            window=self.window,
+        )
+
+
+class LogMelSpectrogram(object):
+    def __init__(
+        self,
+        fs,
+        n_mels,
+        n_fft,
+        n_shift,
+        win_length=None,
+        window="hann",
+        fmin=None,
+        fmax=None,
+        eps=1e-10,
+    ):
+        self.fs = fs
+        self.n_mels = n_mels
+        self.n_fft = n_fft
+        self.n_shift = n_shift
+        self.win_length = win_length
+        self.window = window
+        self.fmin = fmin
+        self.fmax = fmax
+        self.eps = eps
+
+    def __repr__(self):
+        return (
+            "{name}(fs={fs}, n_mels={n_mels}, n_fft={n_fft}, "
+            "n_shift={n_shift}, win_length={win_length}, window={window}, "
+            "fmin={fmin}, fmax={fmax}, eps={eps}))".format(
+                name=self.__class__.__name__,
+                fs=self.fs,
+                n_mels=self.n_mels,
+                n_fft=self.n_fft,
+                n_shift=self.n_shift,
+                win_length=self.win_length,
+                window=self.window,
+                fmin=self.fmin,
+                fmax=self.fmax,
+                eps=self.eps,
+            )
+        )
+
+    def __call__(self, x):
+        return logmelspectrogram(
+            x,
+            fs=self.fs,
+            n_mels=self.n_mels,
+            n_fft=self.n_fft,
+            n_shift=self.n_shift,
+            win_length=self.win_length,
+            window=self.window,
+        )
+
+
+class Stft2LogMelSpectrogram(object):
+    def __init__(self, fs, n_mels, n_fft, fmin=None, fmax=None, eps=1e-10):
+        self.fs = fs
+        self.n_mels = n_mels
+        self.n_fft = n_fft
+        self.fmin = fmin
+        self.fmax = fmax
+        self.eps = eps
+
+    def __repr__(self):
+        return (
+            "{name}(fs={fs}, n_mels={n_mels}, n_fft={n_fft}, "
+            "fmin={fmin}, fmax={fmax}, eps={eps}))".format(
+                name=self.__class__.__name__,
+                fs=self.fs,
+                n_mels=self.n_mels,
+                n_fft=self.n_fft,
+                fmin=self.fmin,
+                fmax=self.fmax,
+                eps=self.eps,
+            )
+        )
+
+    def __call__(self, x):
+        return stft2logmelspectrogram(
+            x,
+            fs=self.fs,
+            n_mels=self.n_mels,
+            n_fft=self.n_fft,
+            fmin=self.fmin,
+            fmax=self.fmax,
+        )
+
+
+class Stft(object):
+    def __init__(
+        self,
+        n_fft,
+        n_shift,
+        win_length=None,
+        window="hann",
+        center=True,
+        pad_mode="reflect",
+    ):
+        self.n_fft = n_fft
+        self.n_shift = n_shift
+        self.win_length = win_length
+        self.window = window
+        self.center = center
+        self.pad_mode = pad_mode
+
+    def __repr__(self):
+        return (
+            "{name}(n_fft={n_fft}, n_shift={n_shift}, "
+            "win_length={win_length}, window={window},"
+            "center={center}, pad_mode={pad_mode})".format(
+                name=self.__class__.__name__,
+                n_fft=self.n_fft,
+                n_shift=self.n_shift,
+                win_length=self.win_length,
+                window=self.window,
+                center=self.center,
+                pad_mode=self.pad_mode,
+            )
+        )
+
+    def __call__(self, x):
+        return stft(
+            x,
+            self.n_fft,
+            self.n_shift,
+            win_length=self.win_length,
+            window=self.window,
+            center=self.center,
+            pad_mode=self.pad_mode,
+        )
+
+
+class IStft(object):
+    def __init__(self, n_shift, win_length=None, window="hann", center=True):
+        self.n_shift = n_shift
+        self.win_length = win_length
+        self.window = window
+        self.center = center
+
+    def __repr__(self):
+        return (
+            "{name}(n_shift={n_shift}, "
+            "win_length={win_length}, window={window},"
+            "center={center})".format(
+                name=self.__class__.__name__,
+                n_shift=self.n_shift,
+                win_length=self.win_length,
+                window=self.window,
+                center=self.center,
+            )
+        )
+
+    def __call__(self, x):
+        return istft(
+            x,
+            self.n_shift,
+            win_length=self.win_length,
+            window=self.window,
+            center=self.center,
+        )
diff --git a/utils/tools.py b/utils/tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..32d4118bd30a063a0315fdd684214447c6a11efa
--- /dev/null
+++ b/utils/tools.py
@@ -0,0 +1,46 @@
+import numpy as np
+import resampy
+import soundfile as sf
+
+from utils.spectrogram import VoicedAreaDetection
+
+def load_wav(wav_path, sr=24000):
+    # wav, fs = librosa.load(wav_path, sr=sr)
+    wav, fs = sf.read(wav_path)
+    if fs != sr:
+        wav = resampy.resample(wav, fs, sr, axis=0)
+        fs = sr
+    # assert fs == sr, f"input audio sample rate must be {sr}Hz. Got {fs}"
+    peak = np.abs(wav).max()
+    if peak > 1.0:
+        wav /= peak
+    return wav, fs
+
+
+def extract_voiced_area(wav_path, hi_freq=1000, hop_size=480, energy_thres=0.5):
+    wav, fs = load_wav(wav_path)
+    voiced_flag = VoicedAreaDetection(
+            x=wav,
+            sr=fs,
+            n_fft=2048,
+            n_shift=hop_size,
+            win_length=2048,
+            hi_freq=hi_freq,
+            energy_thres=energy_thres,
+            )
+    return voiced_flag
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size*dilation - dilation)/2)
+
+
+class AttrDict(dict):
+    def __init__(self, *args, **kwargs):
+        super(AttrDict, self).__init__(*args, **kwargs)
+        self.__dict__ = self
\ No newline at end of file
diff --git a/wavlm/WavLM.py b/wavlm/WavLM.py
new file mode 100644
index 0000000000000000000000000000000000000000..644ab89af21cd90348391d5b09cc7c94ac0d5f6a
--- /dev/null
+++ b/wavlm/WavLM.py
@@ -0,0 +1,743 @@
+# --------------------------------------------------------
+# WavLM: Large-Scale Self-Supervised  Pre-training  for Full Stack Speech Processing (https://arxiv.org/abs/2110.13900.pdf)
+# Github source: https://github.com/microsoft/unilm/tree/master/wavlm
+# Copyright (c) 2021 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Based on fairseq code bases
+# https://github.com/pytorch/fairseq
+# --------------------------------------------------------
+
+import math
+import logging
+from typing import List, Optional, Tuple
+
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import LayerNorm
+from .modules import (
+    Fp32GroupNorm,
+    Fp32LayerNorm,
+    GradMultiply,
+    MultiheadAttention,
+    SamePad,
+    init_bert_params,
+    get_activation_fn,
+    TransposeLast,
+    GLU_Linear,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def compute_mask_indices(
+    shape: Tuple[int, int],
+    padding_mask: Optional[torch.Tensor],
+    mask_prob: float,
+    mask_length: int,
+    mask_type: str = "static",
+    mask_other: float = 0.0,
+    min_masks: int = 0,
+    no_overlap: bool = False,
+    min_space: int = 0,
+) -> np.ndarray:
+    """
+    Computes random mask spans for a given shape
+
+    Args:
+        shape: the the shape for which to compute masks.
+            should be of size 2 where first element is batch size and 2nd is timesteps
+        padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
+        mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
+            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
+            however due to overlaps, the actual number will be smaller (unless no_overlap is True)
+        mask_type: how to compute mask lengths
+            static = fixed size
+            uniform = sample from uniform distribution [mask_other, mask_length*2]
+            normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
+            poisson = sample from possion distribution with lambda = mask length
+        min_masks: minimum number of masked spans
+        no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
+        min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
+    """
+
+    bsz, all_sz = shape
+    mask = np.full((bsz, all_sz), False)
+
+    all_num_mask = int(
+        # add a random number for probabilistic rounding
+        mask_prob * all_sz / float(mask_length)
+        + np.random.rand()
+    )
+
+    all_num_mask = max(min_masks, all_num_mask)
+
+    mask_idcs = []
+    for i in range(bsz):
+        if padding_mask is not None:
+            sz = all_sz - padding_mask[i].long().sum().item()
+            num_mask = int(
+                # add a random number for probabilistic rounding
+                mask_prob * sz / float(mask_length)
+                + np.random.rand()
+            )
+            num_mask = max(min_masks, num_mask)
+        else:
+            sz = all_sz
+            num_mask = all_num_mask
+
+        if mask_type == "static":
+            lengths = np.full(num_mask, mask_length)
+        elif mask_type == "uniform":
+            lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
+        elif mask_type == "normal":
+            lengths = np.random.normal(mask_length, mask_other, size=num_mask)
+            lengths = [max(1, int(round(x))) for x in lengths]
+        elif mask_type == "poisson":
+            lengths = np.random.poisson(mask_length, size=num_mask)
+            lengths = [int(round(x)) for x in lengths]
+        else:
+            raise Exception("unknown mask selection " + mask_type)
+
+        if sum(lengths) == 0:
+            lengths[0] = min(mask_length, sz - 1)
+
+        if no_overlap:
+            mask_idc = []
+
+            def arrange(s, e, length, keep_length):
+                span_start = np.random.randint(s, e - length)
+                mask_idc.extend(span_start + i for i in range(length))
+
+                new_parts = []
+                if span_start - s - min_space >= keep_length:
+                    new_parts.append((s, span_start - min_space + 1))
+                if e - span_start - keep_length - min_space > keep_length:
+                    new_parts.append((span_start + length + min_space, e))
+                return new_parts
+
+            parts = [(0, sz)]
+            min_length = min(lengths)
+            for length in sorted(lengths, reverse=True):
+                lens = np.fromiter(
+                    (e - s if e - s >= length + min_space else 0 for s, e in parts),
+                    np.int,
+                )
+                l_sum = np.sum(lens)
+                if l_sum == 0:
+                    break
+                probs = lens / np.sum(lens)
+                c = np.random.choice(len(parts), p=probs)
+                s, e = parts.pop(c)
+                parts.extend(arrange(s, e, length, min_length))
+            mask_idc = np.asarray(mask_idc)
+        else:
+            min_len = min(lengths)
+            if sz - min_len <= num_mask:
+                min_len = sz - num_mask - 1
+
+            mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
+
+            mask_idc = np.asarray(
+                [
+                    mask_idc[j] + offset
+                    for j in range(len(mask_idc))
+                    for offset in range(lengths[j])
+                ]
+            )
+
+        mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
+
+    min_len = min([len(m) for m in mask_idcs])
+    for i, mask_idc in enumerate(mask_idcs):
+        if len(mask_idc) > min_len:
+            mask_idc = np.random.choice(mask_idc, min_len, replace=False)
+        mask[i, mask_idc] = True
+
+    return mask
+
+
+class WavLMConfig:
+    def __init__(self, cfg=None):
+        self.extractor_mode: str = "default"     # mode for feature extractor. default has a single group norm with d groups in the first conv block, whereas layer_norm has layer norms in every block (meant to use with normalize=True)
+        self.encoder_layers: int = 12     # num encoder layers in the transformer
+
+        self.encoder_embed_dim: int = 768     # encoder embedding dimension
+        self.encoder_ffn_embed_dim: int = 3072     # encoder embedding dimension for FFN
+        self.encoder_attention_heads: int = 12     # num encoder attention heads
+        self.activation_fn: str = "gelu"     # activation function to use
+
+        self.layer_norm_first: bool = False     # apply layernorm first in the transformer
+        self.conv_feature_layers: str = "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2"     # string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...]
+        self.conv_bias: bool = False     # include bias in conv encoder
+        self.feature_grad_mult: float = 1.0     # multiply feature extractor var grads by this
+
+        self.normalize: bool = False  # normalize input to have 0 mean and unit variance during training
+
+        # dropouts
+        self.dropout: float = 0.1     # dropout probability for the transformer
+        self.attention_dropout: float = 0.1     # dropout probability for attention weights
+        self.activation_dropout: float = 0.0     # dropout probability after activation in FFN
+        self.encoder_layerdrop: float = 0.0     # probability of dropping a tarnsformer layer
+        self.dropout_input: float = 0.0     # dropout to apply to the input (after feat extr)
+        self.dropout_features: float = 0.0     # dropout to apply to the features (after feat extr)
+
+        # masking
+        self.mask_length: int = 10     # mask length
+        self.mask_prob: float = 0.65     # probability of replacing a token with mask
+        self.mask_selection: str = "static"     # how to choose mask length
+        self.mask_other: float = 0     # secondary mask argument (used for more complex distributions), see help in compute_mask_indicesh
+        self.no_mask_overlap: bool = False     # whether to allow masks to overlap
+        self.mask_min_space: int = 1     # min space between spans (if no overlap is enabled)
+
+        # channel masking
+        self.mask_channel_length: int = 10     # length of the mask for features (channels)
+        self.mask_channel_prob: float = 0.0     # probability of replacing a feature with 0
+        self.mask_channel_selection: str = "static"     # how to choose mask length for channel masking
+        self.mask_channel_other: float = 0     # secondary mask argument (used for more complex distributions), see help in compute_mask_indices
+        self.no_mask_channel_overlap: bool = False     # whether to allow channel masks to overlap
+        self.mask_channel_min_space: int = 1     # min space between spans (if no overlap is enabled)
+
+        # positional embeddings
+        self.conv_pos: int = 128     # number of filters for convolutional positional embeddings
+        self.conv_pos_groups: int = 16     # number of groups for convolutional positional embedding
+
+        # relative position embedding
+        self.relative_position_embedding: bool = False     # apply relative position embedding
+        self.num_buckets: int = 320     # number of buckets for relative position embedding
+        self.max_distance: int = 1280     # maximum distance for relative position embedding
+        self.gru_rel_pos: bool = False     # apply gated relative position embedding
+
+        if cfg is not None:
+            self.update(cfg)
+
+    def update(self, cfg: dict):
+        self.__dict__.update(cfg)
+
+
+class WavLM(nn.Module):
+    def __init__(
+        self,
+        cfg: WavLMConfig,
+    ) -> None:
+        super().__init__()
+        logger.info(f"WavLM Config: {cfg.__dict__}")
+
+        self.cfg = cfg
+        feature_enc_layers = eval(cfg.conv_feature_layers)
+        self.embed = feature_enc_layers[-1][0]
+
+        self.feature_extractor = ConvFeatureExtractionModel(
+            conv_layers=feature_enc_layers,
+            dropout=0.0,
+            mode=cfg.extractor_mode,
+            conv_bias=cfg.conv_bias,
+        )
+
+        self.post_extract_proj = (
+            nn.Linear(self.embed, cfg.encoder_embed_dim)
+            if self.embed != cfg.encoder_embed_dim
+            else None
+        )
+
+        self.mask_prob = cfg.mask_prob
+        self.mask_selection = cfg.mask_selection
+        self.mask_other = cfg.mask_other
+        self.mask_length = cfg.mask_length
+        self.no_mask_overlap = cfg.no_mask_overlap
+        self.mask_min_space = cfg.mask_min_space
+
+        self.mask_channel_prob = cfg.mask_channel_prob
+        self.mask_channel_selection = cfg.mask_channel_selection
+        self.mask_channel_other = cfg.mask_channel_other
+        self.mask_channel_length = cfg.mask_channel_length
+        self.no_mask_channel_overlap = cfg.no_mask_channel_overlap
+        self.mask_channel_min_space = cfg.mask_channel_min_space
+
+        self.dropout_input = nn.Dropout(cfg.dropout_input)
+        self.dropout_features = nn.Dropout(cfg.dropout_features)
+
+        self.feature_grad_mult = cfg.feature_grad_mult
+
+        self.mask_emb = nn.Parameter(
+            torch.FloatTensor(cfg.encoder_embed_dim).uniform_()
+        )
+
+        self.encoder = TransformerEncoder(cfg)
+        self.layer_norm = LayerNorm(self.embed)
+
+    def apply_mask(self, x, padding_mask):
+        B, T, C = x.shape
+        if self.mask_prob > 0:
+            mask_indices = compute_mask_indices(
+                (B, T),
+                padding_mask,
+                self.mask_prob,
+                self.mask_length,
+                self.mask_selection,
+                self.mask_other,
+                min_masks=2,
+                no_overlap=self.no_mask_overlap,
+                min_space=self.mask_min_space,
+            )
+            mask_indices = torch.from_numpy(mask_indices).to(x.device)
+            x[mask_indices] = self.mask_emb
+        else:
+            mask_indices = None
+
+        if self.mask_channel_prob > 0:
+            mask_channel_indices = compute_mask_indices(
+                (B, C),
+                None,
+                self.mask_channel_prob,
+                self.mask_channel_length,
+                self.mask_channel_selection,
+                self.mask_channel_other,
+                no_overlap=self.no_mask_channel_overlap,
+                min_space=self.mask_channel_min_space,
+            )
+            mask_channel_indices = (
+                torch.from_numpy(mask_channel_indices)
+                .to(x.device)
+                .unsqueeze(1)
+                .expand(-1, T, -1)
+            )
+            x[mask_channel_indices] = 0
+
+        return x, mask_indices
+
+    def forward_padding_mask(
+            self, features: torch.Tensor, padding_mask: torch.Tensor,
+    ) -> torch.Tensor:
+        extra = padding_mask.size(1) % features.size(1)
+        if extra > 0:
+            padding_mask = padding_mask[:, :-extra]
+        padding_mask = padding_mask.view(
+            padding_mask.size(0), features.size(1), -1
+        )
+        padding_mask = padding_mask.all(-1)
+        return padding_mask
+
+    def extract_features(
+        self,
+        source: torch.Tensor,
+        padding_mask: Optional[torch.Tensor] = None,
+        mask: bool = False,
+        ret_conv: bool = False,
+        output_layer: Optional[int] = None,
+        ret_layer_results: bool = False,
+    ):
+
+        if self.feature_grad_mult > 0:
+            features = self.feature_extractor(source)
+            if self.feature_grad_mult != 1.0:
+                features = GradMultiply.apply(features, self.feature_grad_mult)
+        else:
+            with torch.no_grad():
+                features = self.feature_extractor(source)
+
+        features = features.transpose(1, 2)
+        features = self.layer_norm(features)
+
+        if padding_mask is not None:
+            padding_mask = self.forward_padding_mask(features, padding_mask)
+
+        if self.post_extract_proj is not None:
+            features = self.post_extract_proj(features)
+
+        features = self.dropout_input(features)
+
+        if mask:
+            x, mask_indices = self.apply_mask(
+                features, padding_mask
+            )
+        else:
+            x = features
+
+        # feature: (B, T, D), float
+        # target: (B, T), long
+        # x: (B, T, D), float
+        # padding_mask: (B, T), bool
+        # mask_indices: (B, T), bool
+        x, layer_results = self.encoder(
+            x,
+            padding_mask=padding_mask,
+            layer=None if output_layer is None else output_layer - 1
+        )
+
+        res = {"x": x, "padding_mask": padding_mask, "features": features, "layer_results": layer_results}
+
+        feature = res["features"] if ret_conv else res["x"]
+        if ret_layer_results:
+            feature = (feature, res["layer_results"])
+        return feature, res["padding_mask"]
+
+
+class ConvFeatureExtractionModel(nn.Module):
+    def __init__(
+            self,
+            conv_layers: List[Tuple[int, int, int]],
+            dropout: float = 0.0,
+            mode: str = "default",
+            conv_bias: bool = False,
+            conv_type: str = "default"
+    ):
+        super().__init__()
+
+        assert mode in {"default", "layer_norm"}
+
+        def block(
+                n_in,
+                n_out,
+                k,
+                stride,
+                is_layer_norm=False,
+                is_group_norm=False,
+                conv_bias=False,
+        ):
+            def make_conv():
+                conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias)
+                nn.init.kaiming_normal_(conv.weight)
+                return conv
+
+            assert (
+                           is_layer_norm and is_group_norm
+                   ) == False, "layer norm and group norm are exclusive"
+
+            if is_layer_norm:
+                return nn.Sequential(
+                    make_conv(),
+                    nn.Dropout(p=dropout),
+                    nn.Sequential(
+                        TransposeLast(),
+                        Fp32LayerNorm(dim, elementwise_affine=True),
+                        TransposeLast(),
+                    ),
+                    nn.GELU(),
+                )
+            elif is_group_norm:
+                return nn.Sequential(
+                    make_conv(),
+                    nn.Dropout(p=dropout),
+                    Fp32GroupNorm(dim, dim, affine=True),
+                    nn.GELU(),
+                )
+            else:
+                return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU())
+
+        self.conv_type = conv_type
+        if self.conv_type == "default":
+            in_d = 1
+            self.conv_layers = nn.ModuleList()
+            for i, cl in enumerate(conv_layers):
+                assert len(cl) == 3, "invalid conv definition: " + str(cl)
+                (dim, k, stride) = cl
+
+                self.conv_layers.append(
+                    block(
+                        in_d,
+                        dim,
+                        k,
+                        stride,
+                        is_layer_norm=mode == "layer_norm",
+                        is_group_norm=mode == "default" and i == 0,
+                        conv_bias=conv_bias,
+                    )
+                )
+                in_d = dim
+        elif self.conv_type == "conv2d":
+            in_d = 1
+            self.conv_layers = nn.ModuleList()
+            for i, cl in enumerate(conv_layers):
+                assert len(cl) == 3
+                (dim, k, stride) = cl
+
+                self.conv_layers.append(
+                    torch.nn.Conv2d(in_d, dim, k, stride)
+                )
+                self.conv_layers.append(torch.nn.ReLU())
+                in_d = dim
+        elif self.conv_type == "custom":
+            in_d = 1
+            idim = 80
+            self.conv_layers = nn.ModuleList()
+            for i, cl in enumerate(conv_layers):
+                assert len(cl) == 3
+                (dim, k, stride) = cl
+                self.conv_layers.append(
+                    torch.nn.Conv2d(in_d, dim, k, stride, padding=1)
+                )
+                self.conv_layers.append(
+                    torch.nn.LayerNorm([dim, idim])
+                )
+                self.conv_layers.append(torch.nn.ReLU())
+                in_d = dim
+                if (i + 1) % 2 == 0:
+                    self.conv_layers.append(
+                        torch.nn.MaxPool2d(2, stride=2, ceil_mode=True)
+                    )
+                    idim = int(math.ceil(idim / 2))
+        else:
+            pass
+
+    def forward(self, x, mask=None):
+
+        # BxT -> BxCxT
+        x = x.unsqueeze(1)
+        if self.conv_type == "custom":
+            for conv in self.conv_layers:
+                if isinstance(conv, nn.LayerNorm):
+                    x = x.transpose(1, 2)
+                    x = conv(x).transpose(1, 2)
+                else:
+                    x = conv(x)
+            x = x.transpose(2, 3).contiguous()
+            x = x.view(x.size(0), -1, x.size(-1))
+        else:
+            for conv in self.conv_layers:
+                x = conv(x)
+            if self.conv_type == "conv2d":
+                b, c, t, f = x.size()
+                x = x.transpose(2, 3).contiguous().view(b, c * f, t)
+        return x
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+
+        self.dropout = args.dropout
+        self.embedding_dim = args.encoder_embed_dim
+
+        self.pos_conv = nn.Conv1d(
+            self.embedding_dim,
+            self.embedding_dim,
+            kernel_size=args.conv_pos,
+            padding=args.conv_pos // 2,
+            groups=args.conv_pos_groups,
+        )
+        dropout = 0
+        std = math.sqrt((4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim))
+        nn.init.normal_(self.pos_conv.weight, mean=0, std=std)
+        nn.init.constant_(self.pos_conv.bias, 0)
+
+        self.pos_conv = nn.utils.weight_norm(self.pos_conv, name="weight", dim=2)
+        self.pos_conv = nn.Sequential(self.pos_conv, SamePad(args.conv_pos), nn.GELU())
+
+        if hasattr(args, "relative_position_embedding"):
+            self.relative_position_embedding = args.relative_position_embedding
+            self.num_buckets = args.num_buckets
+            self.max_distance = args.max_distance
+        else:
+            self.relative_position_embedding = False
+            self.num_buckets = 0
+            self.max_distance = 0
+
+        self.layers = nn.ModuleList(
+            [
+                TransformerSentenceEncoderLayer(
+                    embedding_dim=self.embedding_dim,
+                    ffn_embedding_dim=args.encoder_ffn_embed_dim,
+                    num_attention_heads=args.encoder_attention_heads,
+                    dropout=self.dropout,
+                    attention_dropout=args.attention_dropout,
+                    activation_dropout=args.activation_dropout,
+                    activation_fn=args.activation_fn,
+                    layer_norm_first=args.layer_norm_first,
+                    has_relative_attention_bias=(self.relative_position_embedding and i == 0),
+                    num_buckets=self.num_buckets,
+                    max_distance=self.max_distance,
+                    gru_rel_pos=args.gru_rel_pos,
+                )
+                for i in range(args.encoder_layers)
+            ]
+        )
+
+        self.layer_norm_first = args.layer_norm_first
+        self.layer_norm = LayerNorm(self.embedding_dim)
+        self.layerdrop = args.encoder_layerdrop
+
+        self.apply(init_bert_params)
+
+    def forward(self, x, padding_mask=None, streaming_mask=None, layer=None):
+        x, layer_results = self.extract_features(x, padding_mask, streaming_mask, layer)
+
+        if self.layer_norm_first and layer is None:
+            x = self.layer_norm(x)
+
+        return x, layer_results
+
+    def extract_features(self, x, padding_mask=None, streaming_mask=None, tgt_layer=None):
+
+        if padding_mask is not None:
+            x[padding_mask] = 0
+
+        x_conv = self.pos_conv(x.transpose(1, 2))
+        x_conv = x_conv.transpose(1, 2)
+        x += x_conv
+
+        if not self.layer_norm_first:
+            x = self.layer_norm(x)
+
+        x = F.dropout(x, p=self.dropout, training=self.training)
+
+        # B x T x C -> T x B x C
+        x = x.transpose(0, 1)
+
+        layer_results = []
+        z = None
+        if tgt_layer is not None:
+            layer_results.append((x, z))
+        r = None
+        pos_bias = None
+        for i, layer in enumerate(self.layers):
+            dropout_probability = np.random.random()
+            if not self.training or (dropout_probability > self.layerdrop):
+                x, z, pos_bias = layer(x, self_attn_padding_mask=padding_mask, need_weights=False,
+                                       self_attn_mask=streaming_mask, pos_bias=pos_bias)
+            if tgt_layer is not None:
+                layer_results.append((x, z))
+            if i == tgt_layer:
+                r = x
+                break
+
+        if r is not None:
+            x = r
+
+        # T x B x C -> B x T x C
+        x = x.transpose(0, 1)
+
+        return x, layer_results
+
+
+class TransformerSentenceEncoderLayer(nn.Module):
+    """
+    Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained
+    models.
+    """
+
+    def __init__(
+            self,
+            embedding_dim: float = 768,
+            ffn_embedding_dim: float = 3072,
+            num_attention_heads: float = 8,
+            dropout: float = 0.1,
+            attention_dropout: float = 0.1,
+            activation_dropout: float = 0.1,
+            activation_fn: str = "relu",
+            layer_norm_first: bool = False,
+            has_relative_attention_bias: bool = False,
+            num_buckets: int = 0,
+            max_distance: int = 0,
+            rescale_init: bool = False,
+            gru_rel_pos: bool = False,
+    ) -> None:
+
+        super().__init__()
+        # Initialize parameters
+        self.embedding_dim = embedding_dim
+        self.dropout = dropout
+        self.activation_dropout = activation_dropout
+
+        # Initialize blocks
+        self.activation_name = activation_fn
+        self.activation_fn = get_activation_fn(activation_fn)
+        self.self_attn = MultiheadAttention(
+            self.embedding_dim,
+            num_attention_heads,
+            dropout=attention_dropout,
+            self_attention=True,
+            has_relative_attention_bias=has_relative_attention_bias,
+            num_buckets=num_buckets,
+            max_distance=max_distance,
+            rescale_init=rescale_init,
+            gru_rel_pos=gru_rel_pos,
+        )
+
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(self.activation_dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        self.layer_norm_first = layer_norm_first
+
+        # layer norm associated with the self attention layer
+        self.self_attn_layer_norm = LayerNorm(self.embedding_dim)
+
+        if self.activation_name == "glu":
+            self.fc1 = GLU_Linear(self.embedding_dim, ffn_embedding_dim, "swish")
+        else:
+            self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
+        self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)
+
+        # layer norm associated with the position wise feed-forward NN
+        self.final_layer_norm = LayerNorm(self.embedding_dim)
+
+    def forward(
+            self,
+            x: torch.Tensor,
+            self_attn_mask: torch.Tensor = None,
+            self_attn_padding_mask: torch.Tensor = None,
+            need_weights: bool = False,
+            pos_bias=None
+    ):
+        """
+        LayerNorm is applied either before or after the self-attention/ffn
+        modules similar to the original Transformer imlementation.
+        """
+        residual = x
+
+        if self.layer_norm_first:
+            x = self.self_attn_layer_norm(x)
+            x, attn, pos_bias = self.self_attn(
+                query=x,
+                key=x,
+                value=x,
+                key_padding_mask=self_attn_padding_mask,
+                need_weights=False,
+                attn_mask=self_attn_mask,
+                position_bias=pos_bias
+            )
+            x = self.dropout1(x)
+            x = residual + x
+
+            residual = x
+            x = self.final_layer_norm(x)
+            if self.activation_name == "glu":
+                x = self.fc1(x)
+            else:
+                x = self.activation_fn(self.fc1(x))
+            x = self.dropout2(x)
+            x = self.fc2(x)
+            x = self.dropout3(x)
+            x = residual + x
+        else:
+            x, attn, pos_bias = self.self_attn(
+                query=x,
+                key=x,
+                value=x,
+                key_padding_mask=self_attn_padding_mask,
+                need_weights=need_weights,
+                attn_mask=self_attn_mask,
+                position_bias=pos_bias
+            )
+
+            x = self.dropout1(x)
+            x = residual + x
+
+            x = self.self_attn_layer_norm(x)
+
+            residual = x
+            if self.activation_name == "glu":
+                x = self.fc1(x)
+            else:
+                x = self.activation_fn(self.fc1(x))
+            x = self.dropout2(x)
+            x = self.fc2(x)
+            x = self.dropout3(x)
+            x = residual + x
+            x = self.final_layer_norm(x)
+
+        return x, attn, pos_bias
+
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diff --git a/wavlm/modules.py b/wavlm/modules.py
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+++ b/wavlm/modules.py
@@ -0,0 +1,827 @@
+# --------------------------------------------------------
+# WavLM: Large-Scale Self-Supervised  Pre-training  for Full Stack Speech Processing (https://arxiv.org/abs/2110.13900.pdf)
+# Github source: https://github.com/microsoft/unilm/tree/master/wavlm
+# Copyright (c) 2021 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Based on fairseq code bases
+# https://github.com/pytorch/fairseq
+# --------------------------------------------------------
+
+import math
+import warnings
+from typing import Dict, Optional, Tuple
+import torch
+from torch import Tensor, nn
+from torch.nn import Parameter
+import torch.nn.functional as F
+
+
+class TransposeLast(nn.Module):
+    def __init__(self, deconstruct_idx=None):
+        super().__init__()
+        self.deconstruct_idx = deconstruct_idx
+
+    def forward(self, x):
+        if self.deconstruct_idx is not None:
+            x = x[self.deconstruct_idx]
+        return x.transpose(-2, -1)
+
+
+class Fp32LayerNorm(nn.LayerNorm):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, input):
+        output = F.layer_norm(
+            input.float(),
+            self.normalized_shape,
+            self.weight.float() if self.weight is not None else None,
+            self.bias.float() if self.bias is not None else None,
+            self.eps,
+        )
+        return output.type_as(input)
+
+
+class Fp32GroupNorm(nn.GroupNorm):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, input):
+        output = F.group_norm(
+            input.float(),
+            self.num_groups,
+            self.weight.float() if self.weight is not None else None,
+            self.bias.float() if self.bias is not None else None,
+            self.eps,
+        )
+        return output.type_as(input)
+
+
+class GradMultiply(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, scale):
+        ctx.scale = scale
+        res = x.new(x)
+        return res
+
+    @staticmethod
+    def backward(ctx, grad):
+        return grad * ctx.scale, None
+
+
+class SamePad(nn.Module):
+    def __init__(self, kernel_size, causal=False):
+        super().__init__()
+        if causal:
+            self.remove = kernel_size - 1
+        else:
+            self.remove = 1 if kernel_size % 2 == 0 else 0
+
+    def forward(self, x):
+        if self.remove > 0:
+            x = x[:, :, : -self.remove]
+        return x
+
+
+class Swish(nn.Module):
+    """Swish function
+    """
+
+    def __init__(self):
+        """Construct an MultiHeadedAttention object."""
+        super(Swish, self).__init__()
+        self.act = torch.nn.Sigmoid()
+
+    def forward(self, x):
+        return x * self.act(x)
+
+
+class GLU_Linear(nn.Module):
+    def __init__(self, input_dim, output_dim, glu_type="sigmoid", bias_in_glu=True):
+        super(GLU_Linear, self).__init__()
+
+        self.glu_type = glu_type
+        self.output_dim = output_dim
+
+        if glu_type == "sigmoid":
+            self.glu_act = torch.nn.Sigmoid()
+        elif glu_type == "swish":
+            self.glu_act = Swish()
+        elif glu_type == "relu":
+            self.glu_act = torch.nn.ReLU()
+        elif glu_type == "gelu":
+            self.glu_act = torch.nn.GELU()
+
+        if bias_in_glu:
+            self.linear = nn.Linear(input_dim, output_dim * 2, True)
+        else:
+            self.linear = nn.Linear(input_dim, output_dim * 2, False)
+
+    def forward(self, x):
+        # to be consistent with GLU_Linear, we assume the input always has the #channel (#dim) in the last dimension of the tensor, so need to switch the dimension first for 1D-Conv case
+        x = self.linear(x)
+
+        if self.glu_type == "bilinear":
+            x = (x[:, :, 0:self.output_dim] * x[:, :, self.output_dim:self.output_dim * 2])
+        else:
+            x = (x[:, :, 0:self.output_dim] * self.glu_act(x[:, :, self.output_dim:self.output_dim * 2]))
+
+        return x
+
+
+def gelu_accurate(x):
+    if not hasattr(gelu_accurate, "_a"):
+        gelu_accurate._a = math.sqrt(2 / math.pi)
+    return (
+        0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3))))
+    )
+
+
+def gelu(x: torch.Tensor) -> torch.Tensor:
+    return torch.nn.functional.gelu(x.float()).type_as(x)
+
+
+def get_activation_fn(activation: str):
+    """Returns the activation function corresponding to `activation`"""
+
+    if activation == "relu":
+        return F.relu
+    elif activation == "gelu":
+        return gelu
+    elif activation == "gelu_fast":
+        warnings.warn(
+            "--activation-fn=gelu_fast has been renamed to gelu_accurate"
+        )
+        return gelu_accurate
+    elif activation == "gelu_accurate":
+        return gelu_accurate
+    elif activation == "tanh":
+        return torch.tanh
+    elif activation == "linear":
+        return lambda x: x
+    elif activation == "glu":
+        return lambda x: x
+    else:
+        raise RuntimeError("--activation-fn {} not supported".format(activation))
+
+
+def init_bert_params(module):
+    """
+    Initialize the weights specific to the BERT Model.
+    This overrides the default initializations depending on the specified arguments.
+        1. If normal_init_linear_weights is set then weights of linear
+           layer will be initialized using the normal distribution and
+           bais will be set to the specified value.
+        2. If normal_init_embed_weights is set then weights of embedding
+           layer will be initialized using the normal distribution.
+        3. If normal_init_proj_weights is set then weights of
+           in_project_weight for MultiHeadAttention initialized using
+           the normal distribution (to be validated).
+    """
+
+    def normal_(data):
+        # with FSDP, module params will be on CUDA, so we cast them back to CPU
+        # so that the RNG is consistent with and without FSDP
+        data.copy_(
+            data.cpu().normal_(mean=0.0, std=0.02).to(data.device)
+        )
+
+    if isinstance(module, nn.Linear):
+        normal_(module.weight.data)
+        if module.bias is not None:
+            module.bias.data.zero_()
+    if isinstance(module, nn.Embedding):
+        normal_(module.weight.data)
+        if module.padding_idx is not None:
+            module.weight.data[module.padding_idx].zero_()
+    if isinstance(module, MultiheadAttention):
+        normal_(module.q_proj.weight.data)
+        normal_(module.k_proj.weight.data)
+        normal_(module.v_proj.weight.data)
+
+
+def quant_noise(module, p, block_size):
+    """
+    Wraps modules and applies quantization noise to the weights for
+    subsequent quantization with Iterative Product Quantization as
+    described in "Training with Quantization Noise for Extreme Model Compression"
+
+    Args:
+        - module: nn.Module
+        - p: amount of Quantization Noise
+        - block_size: size of the blocks for subsequent quantization with iPQ
+
+    Remarks:
+        - Module weights must have the right sizes wrt the block size
+        - Only Linear, Embedding and Conv2d modules are supported for the moment
+        - For more detail on how to quantize by blocks with convolutional weights,
+          see "And the Bit Goes Down: Revisiting the Quantization of Neural Networks"
+        - We implement the simplest form of noise here as stated in the paper
+          which consists in randomly dropping blocks
+    """
+
+    # if no quantization noise, don't register hook
+    if p <= 0:
+        return module
+
+    # supported modules
+    assert isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d))
+
+    # test whether module.weight has the right sizes wrt block_size
+    is_conv = module.weight.ndim == 4
+
+    # 2D matrix
+    if not is_conv:
+        assert (
+            module.weight.size(1) % block_size == 0
+        ), "Input features must be a multiple of block sizes"
+
+    # 4D matrix
+    else:
+        # 1x1 convolutions
+        if module.kernel_size == (1, 1):
+            assert (
+                module.in_channels % block_size == 0
+            ), "Input channels must be a multiple of block sizes"
+        # regular convolutions
+        else:
+            k = module.kernel_size[0] * module.kernel_size[1]
+            assert k % block_size == 0, "Kernel size must be a multiple of block size"
+
+    def _forward_pre_hook(mod, input):
+        # no noise for evaluation
+        if mod.training:
+            if not is_conv:
+                # gather weight and sizes
+                weight = mod.weight
+                in_features = weight.size(1)
+                out_features = weight.size(0)
+
+                # split weight matrix into blocks and randomly drop selected blocks
+                mask = torch.zeros(
+                    in_features // block_size * out_features, device=weight.device
+                )
+                mask.bernoulli_(p)
+                mask = mask.repeat_interleave(block_size, -1).view(-1, in_features)
+
+            else:
+                # gather weight and sizes
+                weight = mod.weight
+                in_channels = mod.in_channels
+                out_channels = mod.out_channels
+
+                # split weight matrix into blocks and randomly drop selected blocks
+                if mod.kernel_size == (1, 1):
+                    mask = torch.zeros(
+                        int(in_channels // block_size * out_channels),
+                        device=weight.device,
+                    )
+                    mask.bernoulli_(p)
+                    mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels)
+                else:
+                    mask = torch.zeros(
+                        weight.size(0), weight.size(1), device=weight.device
+                    )
+                    mask.bernoulli_(p)
+                    mask = (
+                        mask.unsqueeze(2)
+                        .unsqueeze(3)
+                        .repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1])
+                    )
+
+            # scale weights and apply mask
+            mask = mask.to(
+                torch.bool
+            )  # x.bool() is not currently supported in TorchScript
+            s = 1 / (1 - p)
+            mod.weight.data = s * weight.masked_fill(mask, 0)
+
+    module.register_forward_pre_hook(_forward_pre_hook)
+    return module
+
+
+class MultiheadAttention(nn.Module):
+    """Multi-headed attention.
+
+    See "Attention Is All You Need" for more details.
+    """
+
+    def __init__(
+            self,
+            embed_dim,
+            num_heads,
+            kdim=None,
+            vdim=None,
+            dropout=0.0,
+            bias=True,
+            add_bias_kv=False,
+            add_zero_attn=False,
+            self_attention=False,
+            encoder_decoder_attention=False,
+            q_noise=0.0,
+            qn_block_size=8,
+            has_relative_attention_bias=False,
+            num_buckets=32,
+            max_distance=128,
+            gru_rel_pos=False,
+            rescale_init=False,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.kdim = kdim if kdim is not None else embed_dim
+        self.vdim = vdim if vdim is not None else embed_dim
+        self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
+
+        self.num_heads = num_heads
+        self.dropout_module = nn.Dropout(dropout)
+
+        self.has_relative_attention_bias = has_relative_attention_bias
+        self.num_buckets = num_buckets
+        self.max_distance = max_distance
+        if self.has_relative_attention_bias:
+            self.relative_attention_bias = nn.Embedding(num_buckets, num_heads)
+
+        self.head_dim = embed_dim // num_heads
+        self.q_head_dim = self.head_dim
+        self.k_head_dim = self.head_dim
+        assert (
+                self.head_dim * num_heads == self.embed_dim
+        ), "embed_dim must be divisible by num_heads"
+        self.scaling = self.head_dim ** -0.5
+
+        self.self_attention = self_attention
+        self.encoder_decoder_attention = encoder_decoder_attention
+
+        assert not self.self_attention or self.qkv_same_dim, (
+            "Self-attention requires query, key and " "value to be of the same size"
+        )
+
+        k_bias = True
+        if rescale_init:
+            k_bias = False
+
+        k_embed_dim = embed_dim
+        q_embed_dim = embed_dim
+
+        self.k_proj = quant_noise(
+            nn.Linear(self.kdim, k_embed_dim, bias=k_bias), q_noise, qn_block_size
+        )
+        self.v_proj = quant_noise(
+            nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size
+        )
+        self.q_proj = quant_noise(
+            nn.Linear(embed_dim, q_embed_dim, bias=bias), q_noise, qn_block_size
+        )
+
+        self.out_proj = quant_noise(
+            nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size
+        )
+
+        if add_bias_kv:
+            self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
+            self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
+        else:
+            self.bias_k = self.bias_v = None
+
+        self.add_zero_attn = add_zero_attn
+
+        self.gru_rel_pos = gru_rel_pos
+        if self.gru_rel_pos:
+            self.grep_linear = nn.Linear(self.q_head_dim, 8)
+            self.grep_a = nn.Parameter(torch.ones(1, num_heads, 1, 1))
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        if self.qkv_same_dim:
+            # Empirically observed the convergence to be much better with
+            # the scaled initialization
+            nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
+            nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
+            nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
+        else:
+            nn.init.xavier_uniform_(self.k_proj.weight)
+            nn.init.xavier_uniform_(self.v_proj.weight)
+            nn.init.xavier_uniform_(self.q_proj.weight)
+
+        nn.init.xavier_uniform_(self.out_proj.weight)
+        if self.out_proj.bias is not None:
+            nn.init.constant_(self.out_proj.bias, 0.0)
+        if self.bias_k is not None:
+            nn.init.xavier_normal_(self.bias_k)
+        if self.bias_v is not None:
+            nn.init.xavier_normal_(self.bias_v)
+        if self.has_relative_attention_bias:
+            nn.init.xavier_normal_(self.relative_attention_bias.weight)
+
+    def _relative_positions_bucket(self, relative_positions, bidirectional=True):
+        num_buckets = self.num_buckets
+        max_distance = self.max_distance
+        relative_buckets = 0
+
+        if bidirectional:
+            num_buckets = num_buckets // 2
+            relative_buckets += (relative_positions > 0).to(torch.long) * num_buckets
+            relative_positions = torch.abs(relative_positions)
+        else:
+            relative_positions = -torch.min(relative_positions, torch.zeros_like(relative_positions))
+
+        max_exact = num_buckets // 2
+        is_small = relative_positions < max_exact
+
+        relative_postion_if_large = max_exact + (
+                torch.log(relative_positions.float() / max_exact)
+                / math.log(max_distance / max_exact)
+                * (num_buckets - max_exact)
+        ).to(torch.long)
+        relative_postion_if_large = torch.min(
+            relative_postion_if_large, torch.full_like(relative_postion_if_large, num_buckets - 1)
+        )
+
+        relative_buckets += torch.where(is_small, relative_positions, relative_postion_if_large)
+        return relative_buckets
+
+    def compute_bias(self, query_length, key_length):
+        context_position = torch.arange(query_length, dtype=torch.long)[:, None]
+        memory_position = torch.arange(key_length, dtype=torch.long)[None, :]
+        relative_position = memory_position - context_position
+        relative_position_bucket = self._relative_positions_bucket(
+            relative_position,
+            bidirectional=True
+        )
+        relative_position_bucket = relative_position_bucket.to(self.relative_attention_bias.weight.device)
+        values = self.relative_attention_bias(relative_position_bucket)
+        values = values.permute([2, 0, 1])
+        return values
+
+    def forward(
+            self,
+            query,
+            key: Optional[Tensor],
+            value: Optional[Tensor],
+            key_padding_mask: Optional[Tensor] = None,
+            incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
+            need_weights: bool = True,
+            static_kv: bool = False,
+            attn_mask: Optional[Tensor] = None,
+            before_softmax: bool = False,
+            need_head_weights: bool = False,
+            position_bias: Optional[Tensor] = None
+    ) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]:
+        """Input shape: Time x Batch x Channel
+
+        Args:
+            key_padding_mask (ByteTensor, optional): mask to exclude
+                keys that are pads, of shape `(batch, src_len)`, where
+                padding elements are indicated by 1s.
+            need_weights (bool, optional): return the attention weights,
+                averaged over heads (default: False).
+            attn_mask (ByteTensor, optional): typically used to
+                implement causal attention, where the mask prevents the
+                attention from looking forward in time (default: None).
+            before_softmax (bool, optional): return the raw attention
+                weights and values before the attention softmax.
+            need_head_weights (bool, optional): return the attention
+                weights for each head. Implies *need_weights*. Default:
+                return the average attention weights over all heads.
+        """
+        if need_head_weights:
+            need_weights = True
+
+        is_tpu = query.device.type == "xla"
+
+        tgt_len, bsz, embed_dim = query.size()
+        src_len = tgt_len
+        assert embed_dim == self.embed_dim
+        assert list(query.size()) == [tgt_len, bsz, embed_dim]
+        if key is not None:
+            src_len, key_bsz, _ = key.size()
+            if not torch.jit.is_scripting():
+                assert key_bsz == bsz
+                assert value is not None
+                assert src_len, bsz == value.shape[:2]
+
+        if self.has_relative_attention_bias and position_bias is None:
+            position_bias = self.compute_bias(tgt_len, src_len)
+            position_bias = position_bias.unsqueeze(0).repeat(bsz, 1, 1, 1).view(bsz * self.num_heads, tgt_len, src_len)
+
+        if (
+                not is_tpu  # don't use PyTorch version on TPUs
+                and incremental_state is None
+                and not static_kv
+                # A workaround for quantization to work. Otherwise JIT compilation
+                # treats bias in linear module as method.
+                and not torch.jit.is_scripting()
+                and self.q_head_dim == self.head_dim
+        ):
+            assert key is not None and value is not None
+            assert attn_mask is None
+
+            attn_mask_rel_pos = None
+            if position_bias is not None:
+                attn_mask_rel_pos = position_bias
+                if self.gru_rel_pos:
+                    query_layer = query.transpose(0, 1)
+                    new_x_shape = query_layer.size()[:-1] + (self.num_heads, -1)
+                    query_layer = query_layer.view(*new_x_shape)
+                    query_layer = query_layer.permute(0, 2, 1, 3)
+                    _B, _H, _L, __ = query_layer.size()
+
+                    gate_a, gate_b = torch.sigmoid(self.grep_linear(query_layer).view(
+                        _B, _H, _L, 2, 4).sum(-1, keepdim=False)).chunk(2, dim=-1)
+                    gate_a_1 = gate_a * (gate_b * self.grep_a - 1.0) + 2.0
+                    attn_mask_rel_pos = gate_a_1.view(bsz * self.num_heads, -1, 1) * position_bias
+
+                attn_mask_rel_pos = attn_mask_rel_pos.view((-1, tgt_len, tgt_len))
+            k_proj_bias = self.k_proj.bias
+            if k_proj_bias is None:
+                k_proj_bias = torch.zeros_like(self.q_proj.bias)
+
+            x, attn = F.multi_head_attention_forward(
+                query,
+                key,
+                value,
+                self.embed_dim,
+                self.num_heads,
+                torch.empty([0]),
+                torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
+                self.bias_k,
+                self.bias_v,
+                self.add_zero_attn,
+                self.dropout_module.p,
+                self.out_proj.weight,
+                self.out_proj.bias,
+                self.training,
+                # self.training or self.dropout_module.apply_during_inference,
+                key_padding_mask,
+                need_weights,
+                attn_mask_rel_pos,
+                use_separate_proj_weight=True,
+                q_proj_weight=self.q_proj.weight,
+                k_proj_weight=self.k_proj.weight,
+                v_proj_weight=self.v_proj.weight,
+            )
+            return x, attn, position_bias
+
+        if incremental_state is not None:
+            saved_state = self._get_input_buffer(incremental_state)
+            if saved_state is not None and "prev_key" in saved_state:
+                # previous time steps are cached - no need to recompute
+                # key and value if they are static
+                if static_kv:
+                    assert self.encoder_decoder_attention and not self.self_attention
+                    key = value = None
+        else:
+            saved_state = None
+
+        if self.self_attention:
+            q = self.q_proj(query)
+            k = self.k_proj(query)
+            v = self.v_proj(query)
+        elif self.encoder_decoder_attention:
+            # encoder-decoder attention
+            q = self.q_proj(query)
+            if key is None:
+                assert value is None
+                k = v = None
+            else:
+                k = self.k_proj(key)
+                v = self.v_proj(key)
+
+        else:
+            assert key is not None and value is not None
+            q = self.q_proj(query)
+            k = self.k_proj(key)
+            v = self.v_proj(value)
+        q *= self.scaling
+
+        if self.bias_k is not None:
+            assert self.bias_v is not None
+            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
+            v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
+            if attn_mask is not None:
+                attn_mask = torch.cat(
+                    [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
+                )
+            if key_padding_mask is not None:
+                key_padding_mask = torch.cat(
+                    [
+                        key_padding_mask,
+                        key_padding_mask.new_zeros(key_padding_mask.size(0), 1),
+                    ],
+                    dim=1,
+                )
+
+        q = (
+            q.contiguous()
+                .view(tgt_len, bsz * self.num_heads, self.q_head_dim)
+                .transpose(0, 1)
+        )
+        if k is not None:
+            k = (
+                k.contiguous()
+                    .view(-1, bsz * self.num_heads, self.k_head_dim)
+                    .transpose(0, 1)
+            )
+        if v is not None:
+            v = (
+                v.contiguous()
+                    .view(-1, bsz * self.num_heads, self.head_dim)
+                    .transpose(0, 1)
+            )
+
+        if saved_state is not None:
+            # saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
+            if "prev_key" in saved_state:
+                _prev_key = saved_state["prev_key"]
+                assert _prev_key is not None
+                prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim)
+                if static_kv:
+                    k = prev_key
+                else:
+                    assert k is not None
+                    k = torch.cat([prev_key, k], dim=1)
+                src_len = k.size(1)
+            if "prev_value" in saved_state:
+                _prev_value = saved_state["prev_value"]
+                assert _prev_value is not None
+                prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim)
+                if static_kv:
+                    v = prev_value
+                else:
+                    assert v is not None
+                    v = torch.cat([prev_value, v], dim=1)
+            prev_key_padding_mask: Optional[Tensor] = None
+            if "prev_key_padding_mask" in saved_state:
+                prev_key_padding_mask = saved_state["prev_key_padding_mask"]
+            assert k is not None and v is not None
+            key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
+                key_padding_mask=key_padding_mask,
+                prev_key_padding_mask=prev_key_padding_mask,
+                batch_size=bsz,
+                src_len=k.size(1),
+                static_kv=static_kv,
+            )
+
+            saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim)
+            saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim)
+            saved_state["prev_key_padding_mask"] = key_padding_mask
+            # In this branch incremental_state is never None
+            assert incremental_state is not None
+            incremental_state = self._set_input_buffer(incremental_state, saved_state)
+        assert k is not None
+        assert k.size(1) == src_len
+
+        # This is part of a workaround to get around fork/join parallelism
+        # not supporting Optional types.
+        if key_padding_mask is not None and key_padding_mask.dim() == 0:
+            key_padding_mask = None
+
+        if key_padding_mask is not None:
+            assert key_padding_mask.size(0) == bsz
+            assert key_padding_mask.size(1) == src_len
+
+        if self.add_zero_attn:
+            assert v is not None
+            src_len += 1
+            k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
+            v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
+            if attn_mask is not None:
+                attn_mask = torch.cat(
+                    [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
+                )
+            if key_padding_mask is not None:
+                key_padding_mask = torch.cat(
+                    [
+                        key_padding_mask,
+                        torch.zeros(key_padding_mask.size(0), 1).type_as(
+                            key_padding_mask
+                        ),
+                    ],
+                    dim=1,
+                )
+
+        attn_weights = torch.bmm(q, k.transpose(1, 2))
+        attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
+
+        assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
+
+        if attn_mask is not None:
+            attn_mask = attn_mask.unsqueeze(0)
+            attn_weights += attn_mask
+
+        if key_padding_mask is not None:
+            # don't attend to padding symbols
+            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+            if not is_tpu:
+                attn_weights = attn_weights.masked_fill(
+                    key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
+                    float("-inf"),
+                )
+            else:
+                attn_weights = attn_weights.transpose(0, 2)
+                attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf"))
+                attn_weights = attn_weights.transpose(0, 2)
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        if before_softmax:
+            return attn_weights, v, position_bias
+
+        if position_bias is not None:
+            if self.gru_rel_pos == 1:
+                query_layer = q.view(bsz, self.num_heads, tgt_len, self.q_head_dim)
+                _B, _H, _L, __ = query_layer.size()
+                gate_a, gate_b = torch.sigmoid(self.grep_linear(query_layer).view(
+                    _B, _H, _L, 2, 4).sum(-1, keepdim=False)).chunk(2, dim=-1)
+                gate_a_1 = gate_a * (gate_b * self.grep_a - 1.0) + 2.0
+                position_bias = gate_a_1.view(bsz * self.num_heads, -1, 1) * position_bias
+
+            position_bias = position_bias.view(attn_weights.size())
+
+            attn_weights = attn_weights + position_bias
+
+        attn_weights_float = F.softmax(
+            attn_weights, dim=-1
+        )
+        attn_weights = attn_weights_float.type_as(attn_weights)
+        attn_probs = self.dropout_module(attn_weights)
+
+        assert v is not None
+        attn = torch.bmm(attn_probs, v)
+        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
+        attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
+        attn = self.out_proj(attn)
+        attn_weights: Optional[Tensor] = None
+        if need_weights:
+            attn_weights = attn_weights_float.view(
+                bsz, self.num_heads, tgt_len, src_len
+            ).transpose(1, 0)
+            if not need_head_weights:
+                # average attention weights over heads
+                attn_weights = attn_weights.mean(dim=0)
+
+        return attn, attn_weights, position_bias
+
+    @staticmethod
+    def _append_prev_key_padding_mask(
+            key_padding_mask: Optional[Tensor],
+            prev_key_padding_mask: Optional[Tensor],
+            batch_size: int,
+            src_len: int,
+            static_kv: bool,
+    ) -> Optional[Tensor]:
+        # saved key padding masks have shape (bsz, seq_len)
+        if prev_key_padding_mask is not None and static_kv:
+            new_key_padding_mask = prev_key_padding_mask
+        elif prev_key_padding_mask is not None and key_padding_mask is not None:
+            new_key_padding_mask = torch.cat(
+                [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1
+            )
+        # During incremental decoding, as the padding token enters and
+        # leaves the frame, there will be a time when prev or current
+        # is None
+        elif prev_key_padding_mask is not None:
+            if src_len > prev_key_padding_mask.size(1):
+                filler = torch.zeros(
+                    (batch_size, src_len - prev_key_padding_mask.size(1)),
+                    device=prev_key_padding_mask.device,
+                )
+                new_key_padding_mask = torch.cat(
+                    [prev_key_padding_mask.float(), filler.float()], dim=1
+                )
+            else:
+                new_key_padding_mask = prev_key_padding_mask.float()
+        elif key_padding_mask is not None:
+            if src_len > key_padding_mask.size(1):
+                filler = torch.zeros(
+                    (batch_size, src_len - key_padding_mask.size(1)),
+                    device=key_padding_mask.device,
+                )
+                new_key_padding_mask = torch.cat(
+                    [filler.float(), key_padding_mask.float()], dim=1
+                )
+            else:
+                new_key_padding_mask = key_padding_mask.float()
+        else:
+            new_key_padding_mask = prev_key_padding_mask
+        return new_key_padding_mask
+
+    def _get_input_buffer(
+            self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
+    ) -> Dict[str, Optional[Tensor]]:
+        result = self.get_incremental_state(incremental_state, "attn_state")
+        if result is not None:
+            return result
+        else:
+            empty_result: Dict[str, Optional[Tensor]] = {}
+            return empty_result
+
+    def _set_input_buffer(
+            self,
+            incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
+            buffer: Dict[str, Optional[Tensor]],
+    ):
+        return self.set_incremental_state(incremental_state, "attn_state", buffer)
+
+    def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int):
+        return attn_weights