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.env ADDED
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1
+ OPENBLAS_NUM_THREADS = 1
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+ no_proxy = localhost, 127.0.0.1, ::1
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+
4
+ # You can change the location of the model, etc. by changing here
5
+ weight_root = weights
6
+ weight_uvr5_root = uvr5_weights
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+ index_root = logs
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+ rmvpe_root = assets/rmvpe
.gitattributes ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ckpt filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
10
+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.npy filter=lfs diff=lfs merge=lfs -text
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+ *.npz filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tar filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.wasm filter=lfs diff=lfs merge=lfs -text
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+ *.xz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zst filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ stftpitchshift filter=lfs diff=lfs merge=lfs -text
.gitignore ADDED
@@ -0,0 +1,49 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ .DS_Store
2
+ __pycache__
3
+ /TEMP
4
+ /DATASETS
5
+ /RUNTIME
6
+ *.pyd
7
+ hubert_base.pt
8
+ .venv
9
+ alexforkINSTALL.bat
10
+ Changelog_CN.md
11
+ Changelog_EN.md
12
+ Changelog_KO.md
13
+ difdep.py
14
+ EasierGUI.py
15
+ envfilescheck.bat
16
+ export_onnx.py
17
+ .vscode/
18
+ export_onnx_old.py
19
+ ffmpeg.exe
20
+ ffprobe.exe
21
+ Fixes/Launch_Tensorboard.bat
22
+ Fixes/LOCAL_CREPE_FIX.bat
23
+ Fixes/local_fixes.py
24
+ Fixes/tensor-launch.py
25
+ gui.py
26
+ infer-web — backup.py
27
+ infer-webbackup.py
28
+ install_easy_dependencies.py
29
+ install_easyGUI.bat
30
+ installstft.bat
31
+ Launch_Tensorboard.bat
32
+ listdepend.bat
33
+ LOCAL_CREPE_FIX.bat
34
+ local_fixes.py
35
+ oldinfer.py
36
+ onnx_inference_demo.py
37
+ Praat.exe
38
+ requirementsNEW.txt
39
+ rmvpe.pt
40
+ rmvpe.onnx
41
+ run_easiergui.bat
42
+ tensor-launch.py
43
+ values1.json
44
+ 使用需遵守的协议-LICENSE.txt
45
+ !logs/
46
+
47
+ logs/*
48
+ logs/mute/0_gt_wavs/mute40k.spec.pt
49
+ !logs/mute/
Applio-RVC-Fork/utils/README.md ADDED
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1
+ # External Colab Code
2
+ Code used to make Google Colab work correctly
3
+ - Repo link: https://github.com/IAHispano/Applio-RVC-Fork/
4
+
5
+ Thanks to https://github.com/kalomaze/externalcolabcode
6
+
Applio-RVC-Fork/utils/backups.py ADDED
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1
+ import os
2
+ import shutil
3
+ import hashlib
4
+ import time
5
+ import base64
6
+
7
+
8
+
9
+
10
+ LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
11
+ WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
12
+ GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup'
13
+
14
+ def import_google_drive_backup():
15
+ print("Importing Google Drive backup...")
16
+ weights_exist = False
17
+ for root, dirs, files in os.walk(GOOGLE_DRIVE_PATH):
18
+ for filename in files:
19
+ filepath = os.path.join(root, filename)
20
+ if os.path.isfile(filepath) and not filepath.startswith(os.path.join(GOOGLE_DRIVE_PATH, 'weights')):
21
+ backup_filepath = os.path.join(LOGS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
22
+ backup_folderpath = os.path.dirname(backup_filepath)
23
+ if not os.path.exists(backup_folderpath):
24
+ os.makedirs(backup_folderpath)
25
+ print(f'Created backup folder: {backup_folderpath}', flush=True)
26
+ shutil.copy2(filepath, backup_filepath) # copy file with metadata
27
+ print(f'Imported file from Google Drive backup: {filename}')
28
+ elif filepath.startswith(os.path.join(GOOGLE_DRIVE_PATH, 'weights')) and filename.endswith('.pth'):
29
+ weights_exist = True
30
+ weights_filepath = os.path.join(WEIGHTS_FOLDER, os.path.relpath(filepath, os.path.join(GOOGLE_DRIVE_PATH, 'weights')))
31
+ weights_folderpath = os.path.dirname(weights_filepath)
32
+ if not os.path.exists(weights_folderpath):
33
+ os.makedirs(weights_folderpath)
34
+ print(f'Created weights folder: {weights_folderpath}', flush=True)
35
+ shutil.copy2(filepath, weights_filepath) # copy file with metadata
36
+ print(f'Imported file from weights: {filename}')
37
+ if weights_exist:
38
+ print("Copied weights from Google Drive backup to local weights folder.")
39
+ else:
40
+ print("No weights found in Google Drive backup.")
41
+ print("Google Drive backup import completed.")
42
+
43
+ def get_md5_hash(file_path):
44
+ hash_md5 = hashlib.md5()
45
+ with open(file_path, "rb") as f:
46
+ for chunk in iter(lambda: f.read(4096), b""):
47
+ hash_md5.update(chunk)
48
+ return hash_md5.hexdigest()
49
+
50
+ def copy_weights_folder_to_drive():
51
+ destination_folder = os.path.join(GOOGLE_DRIVE_PATH, 'weights')
52
+ try:
53
+ if not os.path.exists(destination_folder):
54
+ os.makedirs(destination_folder)
55
+
56
+ num_copied = 0
57
+ for filename in os.listdir(WEIGHTS_FOLDER):
58
+ if filename.endswith('.pth'):
59
+ source_file = os.path.join(WEIGHTS_FOLDER, filename)
60
+ destination_file = os.path.join(destination_folder, filename)
61
+ if not os.path.exists(destination_file):
62
+ shutil.copy2(source_file, destination_file)
63
+ num_copied += 1
64
+ print(f"Copied {filename} to Google Drive!")
65
+
66
+ if num_copied == 0:
67
+ print("No new finished models found for copying.")
68
+ else:
69
+ print(f"Finished copying {num_copied} files to Google Drive!")
70
+
71
+ except Exception as e:
72
+ print(f"An error occurred while copying weights: {str(e)}")
73
+ # You can log the error or take appropriate actions here.
74
+
75
+ def backup_files():
76
+ print("\nStarting backup loop...")
77
+ last_backup_timestamps_path = os.path.join(LOGS_FOLDER, 'last_backup_timestamps.txt')
78
+ fully_updated = False # boolean to track if all files are up to date
79
+
80
+ while True:
81
+ try:
82
+ updated = False # flag to check if any files were updated
83
+ last_backup_timestamps = {}
84
+
85
+ try:
86
+ with open(last_backup_timestamps_path, 'r') as f:
87
+ last_backup_timestamps = dict(line.strip().split(':') for line in f)
88
+ except FileNotFoundError:
89
+ pass # File does not exist yet, which is fine
90
+
91
+ for root, dirs, files in os.walk(LOGS_FOLDER):
92
+ for filename in files:
93
+ if filename != 'last_backup_timestamps.txt':
94
+ filepath = os.path.join(root, filename)
95
+ if os.path.isfile(filepath):
96
+ backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
97
+ backup_folderpath = os.path.dirname(backup_filepath)
98
+ if not os.path.exists(backup_folderpath):
99
+ os.makedirs(backup_folderpath)
100
+ print(f'Created backup folder: {backup_folderpath}', flush=True)
101
+ # check if file has changed since last backup
102
+ last_backup_timestamp = last_backup_timestamps.get(filepath)
103
+ current_timestamp = os.path.getmtime(filepath)
104
+ if last_backup_timestamp is None or float(last_backup_timestamp) < current_timestamp:
105
+ shutil.copy2(filepath, backup_filepath) # copy file with metadata
106
+ last_backup_timestamps[filepath] = str(current_timestamp) # update last backup timestamp
107
+ if last_backup_timestamp is None:
108
+ print(f'Backed up file: {filename}')
109
+ else:
110
+ print(f'Updating backed up file: {filename}')
111
+ updated = True
112
+ fully_updated = False # if a file is updated, all files are not up to date
113
+
114
+ # check if any files were deleted in Colab and delete them from the backup drive
115
+ for filepath in list(last_backup_timestamps.keys()):
116
+ if not os.path.exists(filepath):
117
+ backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
118
+ if os.path.exists(backup_filepath):
119
+ os.remove(backup_filepath)
120
+ print(f'Deleted file: {filepath}')
121
+ del last_backup_timestamps[filepath]
122
+ updated = True
123
+ fully_updated = False # if a file is deleted, all files are not up to date
124
+
125
+ if not updated and not fully_updated:
126
+ print("Files are up to date.")
127
+ fully_updated = True # if all files are up to date, set the boolean to True
128
+ copy_weights_folder_to_drive()
129
+ sleep_time = 15
130
+ else:
131
+ sleep_time = 0.1
132
+
133
+ with open(last_backup_timestamps_path, 'w') as f:
134
+ for filepath, timestamp in last_backup_timestamps.items():
135
+ f.write(f'{filepath}:{timestamp}\n')
136
+
137
+ time.sleep(sleep_time) # wait for 15 seconds before checking again, or 0.1s if not fully up to date to speed up backups
138
+
139
+ except Exception as e:
140
+ print(f"An error occurred: {str(e)}")
141
+ # You can log the error or take appropriate actions here.
Applio-RVC-Fork/utils/backups_test.py ADDED
@@ -0,0 +1,138 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import os
3
+ import shutil
4
+ import hashlib
5
+ import time
6
+
7
+ LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
8
+ WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
9
+ GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup'
10
+
11
+ def import_google_drive_backup():
12
+ print("Importing Google Drive backup...")
13
+ GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup' # change this to your Google Drive path
14
+ LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
15
+ WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
16
+ weights_exist = False
17
+ files_to_copy = []
18
+ weights_to_copy = []
19
+
20
+ def handle_files(root, files, is_weight_files=False):
21
+ for filename in files:
22
+ filepath = os.path.join(root, filename)
23
+ if filename.endswith('.pth') and is_weight_files:
24
+ weights_exist = True
25
+ backup_filepath = os.path.join(WEIGHTS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
26
+ else:
27
+ backup_filepath = os.path.join(LOGS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
28
+ backup_folderpath = os.path.dirname(backup_filepath)
29
+ if not os.path.exists(backup_folderpath):
30
+ os.makedirs(backup_folderpath)
31
+ print(f'Created folder: {backup_folderpath}', flush=True)
32
+ if is_weight_files:
33
+ weights_to_copy.append((filepath, backup_filepath))
34
+ else:
35
+ files_to_copy.append((filepath, backup_filepath))
36
+
37
+ for root, dirs, files in os.walk(os.path.join(GOOGLE_DRIVE_PATH, 'logs')):
38
+ handle_files(root, files)
39
+
40
+ for root, dirs, files in os.walk(os.path.join(GOOGLE_DRIVE_PATH, 'weights')):
41
+ handle_files(root, files, True)
42
+
43
+ # Copy files in batches
44
+ total_files = len(files_to_copy)
45
+ start_time = time.time()
46
+ for i, (source, dest) in enumerate(files_to_copy, start=1):
47
+ with open(source, 'rb') as src, open(dest, 'wb') as dst:
48
+ shutil.copyfileobj(src, dst, 1024*1024) # 1MB buffer size
49
+ # Report progress every 5 seconds or after every 100 files, whichever is less frequent
50
+ if time.time() - start_time > 5 or i % 100 == 0:
51
+ print(f'\rCopying file {i} of {total_files} ({i * 100 / total_files:.2f}%)', end="")
52
+ start_time = time.time()
53
+ print(f'\nImported {len(files_to_copy)} files from Google Drive backup')
54
+
55
+ # Copy weights in batches
56
+ total_weights = len(weights_to_copy)
57
+ start_time = time.time()
58
+ for i, (source, dest) in enumerate(weights_to_copy, start=1):
59
+ with open(source, 'rb') as src, open(dest, 'wb') as dst:
60
+ shutil.copyfileobj(src, dst, 1024*1024) # 1MB buffer size
61
+ # Report progress every 5 seconds or after every 100 files, whichever is less frequent
62
+ if time.time() - start_time > 5 or i % 100 == 0:
63
+ print(f'\rCopying weight file {i} of {total_weights} ({i * 100 / total_weights:.2f}%)', end="")
64
+ start_time = time.time()
65
+ if weights_exist:
66
+ print(f'\nImported {len(weights_to_copy)} weight files')
67
+ print("Copied weights from Google Drive backup to local weights folder.")
68
+ else:
69
+ print("\nNo weights found in Google Drive backup.")
70
+ print("Google Drive backup import completed.")
71
+
72
+ def backup_files():
73
+ print("\n Starting backup loop...")
74
+ last_backup_timestamps_path = os.path.join(LOGS_FOLDER, 'last_backup_timestamps.txt')
75
+ fully_updated = False # boolean to track if all files are up to date
76
+ try:
77
+ with open(last_backup_timestamps_path, 'r') as f:
78
+ last_backup_timestamps = dict(line.strip().split(':') for line in f)
79
+ except:
80
+ last_backup_timestamps = {}
81
+
82
+ while True:
83
+ updated = False
84
+ files_to_copy = []
85
+ files_to_delete = []
86
+
87
+ for root, dirs, files in os.walk(LOGS_FOLDER):
88
+ for filename in files:
89
+ if filename != 'last_backup_timestamps.txt':
90
+ filepath = os.path.join(root, filename)
91
+ if os.path.isfile(filepath):
92
+ backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
93
+ backup_folderpath = os.path.dirname(backup_filepath)
94
+
95
+ if not os.path.exists(backup_folderpath):
96
+ os.makedirs(backup_folderpath)
97
+ print(f'Created backup folder: {backup_folderpath}', flush=True)
98
+
99
+ # check if file has changed since last backup
100
+ last_backup_timestamp = last_backup_timestamps.get(filepath)
101
+ current_timestamp = os.path.getmtime(filepath)
102
+ if last_backup_timestamp is None or float(last_backup_timestamp) < current_timestamp:
103
+ files_to_copy.append((filepath, backup_filepath)) # add to list of files to copy
104
+ last_backup_timestamps[filepath] = str(current_timestamp) # update last backup timestamp
105
+ updated = True
106
+ fully_updated = False # if a file is updated, all files are not up to date
107
+
108
+ # check if any files were deleted in Colab and delete them from the backup drive
109
+ for filepath in list(last_backup_timestamps.keys()):
110
+ if not os.path.exists(filepath):
111
+ backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
112
+ if os.path.exists(backup_filepath):
113
+ files_to_delete.append(backup_filepath) # add to list of files to delete
114
+ del last_backup_timestamps[filepath]
115
+ updated = True
116
+ fully_updated = False # if a file is deleted, all files are not up to date
117
+
118
+ # Copy files in batches
119
+ if files_to_copy:
120
+ for source, dest in files_to_copy:
121
+ shutil.copy2(source, dest)
122
+ print(f'Copied or updated {len(files_to_copy)} files')
123
+
124
+ # Delete files in batches
125
+ if files_to_delete:
126
+ for file in files_to_delete:
127
+ os.remove(file)
128
+ print(f'Deleted {len(files_to_delete)} files')
129
+
130
+ if not updated and not fully_updated:
131
+ print("Files are up to date.")
132
+ fully_updated = True # if all files are up to date, set the boolean to True
133
+ copy_weights_folder_to_drive()
134
+
135
+ with open(last_backup_timestamps_path, 'w') as f:
136
+ for filepath, timestamp in last_backup_timestamps.items():
137
+ f.write(f'{filepath}:{timestamp}\n')
138
+ time.sleep(15) # wait for 15 seconds before checking again
Applio-RVC-Fork/utils/clonerepo_experimental.py ADDED
@@ -0,0 +1,253 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import subprocess
3
+ import shutil
4
+ from concurrent.futures import ThreadPoolExecutor, as_completed
5
+ from tqdm.notebook import tqdm
6
+ from pathlib import Path
7
+ import requests
8
+
9
+ def run_script():
10
+ def run_cmd(cmd):
11
+ process = subprocess.run(cmd, shell=True, check=True, text=True)
12
+ return process.stdout
13
+
14
+ # Change the current directory to /content/
15
+ os.chdir('/content/')
16
+ print("Changing dir to /content/")
17
+
18
+ # Your function to edit the file
19
+ def edit_file(file_path):
20
+ temp_file_path = "/tmp/temp_file.py"
21
+ changes_made = False
22
+ with open(file_path, "r") as file, open(temp_file_path, "w") as temp_file:
23
+ previous_line = ""
24
+ second_previous_line = ""
25
+ for line in file:
26
+ new_line = line.replace("value=160", "value=128")
27
+ if new_line != line:
28
+ print("Replaced 'value=160' with 'value=128'")
29
+ changes_made = True
30
+ line = new_line
31
+
32
+ new_line = line.replace("crepe hop length: 160", "crepe hop length: 128")
33
+ if new_line != line:
34
+ print("Replaced 'crepe hop length: 160' with 'crepe hop length: 128'")
35
+ changes_made = True
36
+ line = new_line
37
+
38
+ new_line = line.replace("value=0.88", "value=0.75")
39
+ if new_line != line:
40
+ print("Replaced 'value=0.88' with 'value=0.75'")
41
+ changes_made = True
42
+ line = new_line
43
+
44
+ if "label=i18n(\"输入源音量包络替换输出音量包络融合比例,越靠近1越使用输出包络\")" in previous_line and "value=1," in line:
45
+ new_line = line.replace("value=1,", "value=0.25,")
46
+ if new_line != line:
47
+ print("Replaced 'value=1,' with 'value=0.25,' based on the condition")
48
+ changes_made = True
49
+ line = new_line
50
+
51
+ if "label=i18n(\"总训练轮数total_epoch\")" in previous_line and "value=20," in line:
52
+ new_line = line.replace("value=20,", "value=500,")
53
+ if new_line != line:
54
+ print("Replaced 'value=20,' with 'value=500,' based on the condition for DEFAULT EPOCH")
55
+ changes_made = True
56
+ line = new_line
57
+
58
+ if 'choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny"], # Fork Feature. Add Crepe-Tiny' in previous_line:
59
+ if 'value="pm",' in line:
60
+ new_line = line.replace('value="pm",', 'value="mangio-crepe",')
61
+ if new_line != line:
62
+ print("Replaced 'value=\"pm\",' with 'value=\"mangio-crepe\",' based on the condition")
63
+ changes_made = True
64
+ line = new_line
65
+
66
+ new_line = line.replace('label=i18n("输入训练文件夹路径"), value="E:\\\\语音音频+标注\\\\米津玄师\\\\src"', 'label=i18n("输入训练文件夹路径"), value="/content/dataset/"')
67
+ if new_line != line:
68
+ print("Replaced 'label=i18n(\"输入训练文件夹路径\"), value=\"E:\\\\语音音频+标注\\\\米津玄师\\\\src\"' with 'label=i18n(\"输入训练文件夹路径\"), value=\"/content/dataset/\"'")
69
+ changes_made = True
70
+ line = new_line
71
+
72
+ if 'label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"),' in second_previous_line:
73
+ if 'value=i18n("否"),' in line:
74
+ new_line = line.replace('value=i18n("否"),', 'value=i18n("是"),')
75
+ if new_line != line:
76
+ print("Replaced 'value=i18n(\"否\"),' with 'value=i18n(\"是\"),' based on the condition for SAVE ONLY LATEST")
77
+ changes_made = True
78
+ line = new_line
79
+
80
+ if 'label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),' in second_previous_line:
81
+ if 'value=i18n("否"),' in line:
82
+ new_line = line.replace('value=i18n("否"),', 'value=i18n("是"),')
83
+ if new_line != line:
84
+ print("Replaced 'value=i18n(\"否\"),' with 'value=i18n(\"是\"),' based on the condition for SAVE SMALL WEIGHTS")
85
+ changes_made = True
86
+ line = new_line
87
+
88
+ temp_file.write(line)
89
+ second_previous_line = previous_line
90
+ previous_line = line
91
+
92
+ # After finished, we replace the original file with the temp one
93
+ import shutil
94
+ shutil.move(temp_file_path, file_path)
95
+
96
+ if changes_made:
97
+ print("Changes made and file saved successfully.")
98
+ else:
99
+ print("No changes were needed.")
100
+
101
+ # Define the repo path
102
+ repo_path = '/content/Applio-RVC-Fork'
103
+
104
+ def copy_all_files_in_directory(src_dir, dest_dir):
105
+ # Iterate over all files in source directory
106
+ for item in Path(src_dir).glob('*'):
107
+ if item.is_file():
108
+ # Copy each file to destination directory
109
+ shutil.copy(item, dest_dir)
110
+ else:
111
+ # If it's a directory, make a new directory in the destination and copy the files recursively
112
+ new_dest = Path(dest_dir) / item.name
113
+ new_dest.mkdir(exist_ok=True)
114
+ copy_all_files_in_directory(str(item), str(new_dest))
115
+
116
+ def clone_and_copy_repo(repo_path):
117
+ # New repository link
118
+ new_repo_link = "https://github.com/IAHispano/Applio-RVC-Fork/"
119
+ # Temporary path to clone the repository
120
+ temp_repo_path = "/content/temp_Applio-RVC-Fork"
121
+ # New folder name
122
+ new_folder_name = "Applio-RVC-Fork"
123
+
124
+ # Clone the latest code from the new repository to a temporary location
125
+ run_cmd(f"git clone {new_repo_link} {temp_repo_path}")
126
+ os.chdir(temp_repo_path)
127
+
128
+ run_cmd(f"git checkout 3fa4dad3d8961e5ca2522e9e12c0b4ddb71ad402")
129
+ run_cmd(f"git checkout f9e606c279cb49420597519b0a83b92be81e42e4")
130
+ run_cmd(f"git checkout 9e305588844c5442d58add1061b29beeca89d679")
131
+ run_cmd(f"git checkout bf92dc1eb54b4f28d6396a4d1820a25896cc9af8")
132
+ run_cmd(f"git checkout c3810e197d3cb98039973b2f723edf967ecd9e61")
133
+ run_cmd(f"git checkout a33159efd134c2413b0afe26a76b7dc87926d2de")
134
+ run_cmd(f"git checkout 24e251fb62c662e39ac5cf9253cc65deb9be94ec")
135
+ run_cmd(f"git checkout ad5667d3017e93232dba85969cddac1322ba2902")
136
+ run_cmd(f"git checkout ce9715392cf52dd5a0e18e00d1b5e408f08dbf27")
137
+ run_cmd(f"git checkout 7c7da3f2ac68f3bd8f3ad5ca5c700f18ab9f90eb")
138
+ run_cmd(f"git checkout 4ac395eab101955e8960b50d772c26f592161764")
139
+ run_cmd(f"git checkout b15b358702294c7375761584e5276c811ffab5e8")
140
+ run_cmd(f"git checkout 1501793dc490982db9aca84a50647764caa66e51")
141
+ run_cmd(f"git checkout 21f7faf57219c75e6ba837062350391a803e9ae2")
142
+ run_cmd(f"git checkout b5eb689fbc409b49f065a431817f822f554cebe7")
143
+ run_cmd(f"git checkout 7e02fae1ebf24cb151bf6cbe787d06734aa65862")
144
+ run_cmd(f"git checkout 6aea5ea18ed0b9a1e03fa5d268d6bc3c616672a9")
145
+ run_cmd(f"git checkout f0f9b25717e59116473fb42bd7f9252cfc32b398")
146
+ run_cmd(f"git checkout b394de424088a81fc081224bc27338a8651ad3b2")
147
+ run_cmd(f"git checkout f1999406a88b80c965d2082340f5ea2bfa9ab67a")
148
+ run_cmd(f"git checkout d98a0fa8dc715308dfc73eac5c553b69c6ee072b")
149
+ run_cmd(f"git checkout d73267a415fb0eba98477afa43ef71ffd82a7157")
150
+ run_cmd(f"git checkout 1a03d01356ae79179e1fb8d8915dc9cc79925742")
151
+ run_cmd(f"git checkout 81497bb3115e92c754300c9b3992df428886a3e9")
152
+ run_cmd(f"git checkout c5af1f8edcf79cb70f065c0110e279e78e48caf9")
153
+ run_cmd(f"git checkout cdb3c90109387fa4dfa92f53c3864c71170ffc77")
154
+
155
+ # Edit the file here, before copying
156
+ #edit_file(f"{temp_repo_path}/infer-web.py")
157
+
158
+ # Copy all files from the cloned repository to the existing path
159
+ copy_all_files_in_directory(temp_repo_path, repo_path)
160
+ print(f"Copying all {new_folder_name} files from GitHub.")
161
+
162
+ # Change working directory back to /content/
163
+ os.chdir('/content/')
164
+ print("Changed path back to /content/")
165
+
166
+ # Remove the temporary cloned repository
167
+ shutil.rmtree(temp_repo_path)
168
+
169
+ # Call the function
170
+ clone_and_copy_repo(repo_path)
171
+
172
+ # Download the credentials file for RVC archive sheet
173
+ os.makedirs('/content/Applio-RVC-Fork/stats/', exist_ok=True)
174
+ run_cmd("wget -q https://cdn.discordapp.com/attachments/945486970883285045/1114717554481569802/peppy-generator-388800-07722f17a188.json -O /content/Applio-RVC-Fork/stats/peppy-generator-388800-07722f17a188.json")
175
+
176
+ # Forcefully delete any existing torchcrepe dependencies downloaded from an earlier run just in case
177
+ shutil.rmtree('/content/Applio-RVC-Fork/torchcrepe', ignore_errors=True)
178
+ shutil.rmtree('/content/torchcrepe', ignore_errors=True)
179
+
180
+ # Download the torchcrepe folder from the maxrmorrison/torchcrepe repository
181
+ run_cmd("git clone https://github.com/maxrmorrison/torchcrepe.git")
182
+ shutil.move('/content/torchcrepe/torchcrepe', '/content/Applio-RVC-Fork/')
183
+ shutil.rmtree('/content/torchcrepe', ignore_errors=True) # Delete the torchcrepe repository folder
184
+
185
+ # Change the current directory to /content/Applio-RVC-Fork
186
+ os.chdir('/content/Applio-RVC-Fork')
187
+ os.makedirs('pretrained', exist_ok=True)
188
+ os.makedirs('uvr5_weights', exist_ok=True)
189
+
190
+ def download_file(url, filepath):
191
+ response = requests.get(url, stream=True)
192
+ response.raise_for_status()
193
+
194
+ with open(filepath, "wb") as file:
195
+ for chunk in response.iter_content(chunk_size=8192):
196
+ if chunk:
197
+ file.write(chunk)
198
+
199
+ def download_pretrained_models():
200
+ pretrained_models = {
201
+ "pretrained": [
202
+ "D40k.pth",
203
+ "G40k.pth",
204
+ "f0D40k.pth",
205
+ "f0G40k.pth"
206
+ ],
207
+ "pretrained_v2": [
208
+ "D40k.pth",
209
+ "G40k.pth",
210
+ "f0D40k.pth",
211
+ "f0G40k.pth",
212
+ "f0G48k.pth",
213
+ "f0D48k.pth"
214
+ ],
215
+ "uvr5_weights": [
216
+ "HP2-人声vocals+非人声instrumentals.pth",
217
+ "HP5-主旋律人声vocals+其他instrumentals.pth",
218
+ "VR-DeEchoNormal.pth",
219
+ "VR-DeEchoDeReverb.pth",
220
+ "VR-DeEchoAggressive.pth",
221
+ "HP5_only_main_vocal.pth",
222
+ "HP3_all_vocals.pth",
223
+ "HP2_all_vocals.pth"
224
+ ]
225
+ }
226
+ part2 = "I"
227
+ base_url = "https://huggingface.co/lj1995/VoiceConversionWebU" + part2 + "/resolve/main/"
228
+ base_path = "/content/Applio-RVC-Fork/"
229
+ base_pathm = base_path
230
+
231
+ # Calculate total number of files to download
232
+ total_files = sum(len(files) for files in pretrained_models.values()) + 1 # +1 for hubert_base.pt
233
+
234
+ with tqdm(total=total_files, desc="Downloading files") as pbar:
235
+ for folder, models in pretrained_models.items():
236
+ folder_path = os.path.join(base_path, folder)
237
+ os.makedirs(folder_path, exist_ok=True)
238
+ for model in models:
239
+ url = base_url + folder + "/" + model
240
+ filepath = os.path.join(folder_path, model)
241
+ download_file(url, filepath)
242
+ pbar.update()
243
+
244
+ # Download hubert_base.pt to the base path
245
+ hubert_url = base_url + "hubert_base.pt"
246
+ hubert_filepath = os.path.join(base_pathm, "hubert_base.pt")
247
+ download_file(hubert_url, hubert_filepath)
248
+ pbar.update()
249
+ def clone_repository(run_download):
250
+ with ThreadPoolExecutor(max_workers=2) as executor:
251
+ executor.submit(run_script)
252
+ if run_download:
253
+ executor.submit(download_pretrained_models)
Applio-RVC-Fork/utils/dependency.py ADDED
@@ -0,0 +1,170 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import csv
3
+ import shutil
4
+ import tarfile
5
+ import subprocess
6
+ from pathlib import Path
7
+ from datetime import datetime
8
+
9
+ def install_packages_but_jank_af():
10
+ packages = ['build-essential', 'python3-dev', 'ffmpeg', 'aria2']
11
+ pip_packages = ['pip', 'setuptools', 'wheel', 'httpx==0.23.0', 'faiss-gpu', 'fairseq', 'gradio==3.34.0',
12
+ 'ffmpeg', 'ffmpeg-python', 'praat-parselmouth', 'pyworld', 'numpy==1.23.5',
13
+ 'numba==0.56.4', 'librosa==0.9.2', 'mega.py', 'gdown', 'onnxruntime', 'pyngrok==4.1.12',
14
+ 'gTTS', 'elevenlabs', 'wget', 'tensorboardX', 'unidecode', 'huggingface-hub', 'stftpitchshift==1.5.1',
15
+ 'yt-dlp', 'pedalboard', 'pathvalidate', 'nltk', 'edge-tts', 'git+https://github.com/suno-ai/bark.git', 'python-dotenv' , 'av']
16
+
17
+ print("Updating and installing system packages...")
18
+ for package in packages:
19
+ print(f"Installing {package}...")
20
+ subprocess.check_call(['apt-get', 'install', '-qq', '-y', package])
21
+
22
+ print("Updating and installing pip packages...")
23
+ subprocess.check_call(['pip', 'install', '--upgrade'] + pip_packages)
24
+
25
+ print('Packages up to date.')
26
+
27
+
28
+ def setup_environment(ForceUpdateDependencies, ForceTemporaryStorage):
29
+ # Mounting Google Drive
30
+ if not ForceTemporaryStorage:
31
+ from google.colab import drive
32
+
33
+ if not os.path.exists('/content/drive'):
34
+ drive.mount('/content/drive')
35
+ else:
36
+ print('Drive is already mounted. Proceeding...')
37
+
38
+ # Function to install dependencies with progress
39
+ def install_packages():
40
+ packages = ['build-essential', 'python3-dev', 'ffmpeg', 'aria2']
41
+ pip_packages = ['pip', 'setuptools', 'wheel', 'httpx==0.23.0', 'faiss-gpu', 'fairseq', 'gradio==3.34.0',
42
+ 'ffmpeg', 'ffmpeg-python', 'praat-parselmouth', 'pyworld', 'numpy==1.23.5',
43
+ 'numba==0.56.4', 'librosa==0.9.2', 'mega.py', 'gdown', 'onnxruntime', 'pyngrok==4.1.12',
44
+ 'gTTS', 'elevenlabs', 'wget', 'tensorboardX', 'unidecode', 'huggingface-hub', 'stftpitchshift==1.5.1',
45
+ 'yt-dlp', 'pedalboard', 'pathvalidate', 'nltk', 'edge-tts', 'git+https://github.com/suno-ai/bark.git', 'python-dotenv' , 'av']
46
+
47
+ print("Updating and installing system packages...")
48
+ for package in packages:
49
+ print(f"Installing {package}...")
50
+ subprocess.check_call(['apt-get', 'install', '-qq', '-y', package])
51
+
52
+ print("Updating and installing pip packages...")
53
+ subprocess.check_call(['pip', 'install', '--upgrade'] + pip_packages)
54
+
55
+
56
+ print('Packages up to date.')
57
+
58
+ # Function to scan a directory and writes filenames and timestamps
59
+ def scan_and_write(base_path, output_file):
60
+ with open(output_file, 'w', newline='') as f:
61
+ writer = csv.writer(f)
62
+ for dirpath, dirs, files in os.walk(base_path):
63
+ for filename in files:
64
+ fname = os.path.join(dirpath, filename)
65
+ try:
66
+ mtime = os.path.getmtime(fname)
67
+ writer.writerow([fname, mtime])
68
+ except Exception as e:
69
+ print(f'Skipping irrelevant nonexistent file {fname}: {str(e)}')
70
+ print(f'Finished recording filesystem timestamps to {output_file}.')
71
+
72
+ # Function to compare files
73
+ def compare_files(old_file, new_file):
74
+ old_files = {}
75
+ new_files = {}
76
+
77
+ with open(old_file, 'r') as f:
78
+ reader = csv.reader(f)
79
+ old_files = {rows[0]:rows[1] for rows in reader}
80
+
81
+ with open(new_file, 'r') as f:
82
+ reader = csv.reader(f)
83
+ new_files = {rows[0]:rows[1] for rows in reader}
84
+
85
+ removed_files = old_files.keys() - new_files.keys()
86
+ added_files = new_files.keys() - old_files.keys()
87
+ unchanged_files = old_files.keys() & new_files.keys()
88
+
89
+ changed_files = {f for f in unchanged_files if old_files[f] != new_files[f]}
90
+
91
+ for file in removed_files:
92
+ print(f'File has been removed: {file}')
93
+
94
+ for file in changed_files:
95
+ print(f'File has been updated: {file}')
96
+
97
+ return list(added_files) + list(changed_files)
98
+
99
+ # Check if CachedRVC.tar.gz exists
100
+ if ForceTemporaryStorage:
101
+ file_path = '/content/CachedRVC.tar.gz'
102
+ else:
103
+ file_path = '/content/drive/MyDrive/RVC_Cached/CachedRVC.tar.gz'
104
+
105
+ content_file_path = '/content/CachedRVC.tar.gz'
106
+ extract_path = '/'
107
+
108
+ if not os.path.exists(file_path):
109
+ folder_path = os.path.dirname(file_path)
110
+ os.makedirs(folder_path, exist_ok=True)
111
+ print('No cached dependency install found. Attempting to download GitHub backup..')
112
+
113
+ try:
114
+ download_url = "https://github.com/kalomaze/QuickMangioFixes/releases/download/release3/CachedRVC.tar.gz"
115
+ subprocess.run(["wget", "-O", file_path, download_url])
116
+ print('Download completed successfully!')
117
+ except Exception as e:
118
+ print('Download failed:', str(e))
119
+
120
+ # Delete the failed download file
121
+ if os.path.exists(file_path):
122
+ os.remove(file_path)
123
+ print('Failed download file deleted. Continuing manual backup..')
124
+
125
+ if Path(file_path).exists():
126
+ if ForceTemporaryStorage:
127
+ print('Finished downloading CachedRVC.tar.gz.')
128
+ else:
129
+ print('CachedRVC.tar.gz found on Google Drive. Proceeding to copy and extract...')
130
+
131
+ # Check if ForceTemporaryStorage is True and skip copying if it is
132
+ if ForceTemporaryStorage:
133
+ pass
134
+ else:
135
+ shutil.copy(file_path, content_file_path)
136
+
137
+ print('Beginning backup copy operation...')
138
+
139
+ with tarfile.open(content_file_path, 'r:gz') as tar:
140
+ for member in tar.getmembers():
141
+ target_path = os.path.join(extract_path, member.name)
142
+ try:
143
+ tar.extract(member, extract_path)
144
+ except Exception as e:
145
+ print('Failed to extract a file (this isn\'t normal)... forcing an update to compensate')
146
+ ForceUpdateDependencies = True
147
+ print(f'Extraction of {content_file_path} to {extract_path} completed.')
148
+
149
+ if ForceUpdateDependencies:
150
+ install_packages()
151
+ ForceUpdateDependencies = False
152
+ else:
153
+ print('CachedRVC.tar.gz not found. Proceeding to create an index of all current files...')
154
+ scan_and_write('/usr/', '/content/usr_files.csv')
155
+
156
+ install_packages()
157
+
158
+ scan_and_write('/usr/', '/content/usr_files_new.csv')
159
+ changed_files = compare_files('/content/usr_files.csv', '/content/usr_files_new.csv')
160
+
161
+ with tarfile.open('/content/CachedRVC.tar.gz', 'w:gz') as new_tar:
162
+ for file in changed_files:
163
+ new_tar.add(file)
164
+ print(f'Added to tar: {file}')
165
+
166
+ os.makedirs('/content/drive/MyDrive/RVC_Cached', exist_ok=True)
167
+ shutil.copy('/content/CachedRVC.tar.gz', '/content/drive/MyDrive/RVC_Cached/CachedRVC.tar.gz')
168
+ print('Updated CachedRVC.tar.gz copied to Google Drive.')
169
+ print('Dependencies fully up to date; future runs should be faster.')
170
+
Applio-RVC-Fork/utils/i18n.py ADDED
@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import locale
2
+ import json
3
+ import os
4
+
5
+
6
+ def load_language_list(language):
7
+ with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
8
+ language_list = json.load(f)
9
+ return language_list
10
+
11
+
12
+ class I18nAuto:
13
+ def __init__(self, language=None):
14
+ if language in ["Auto", None]:
15
+ language = "es_ES"
16
+ if not os.path.exists(f"./i18n/{language}.json"):
17
+ language = "es_ES"
18
+ language = "es_ES"
19
+ self.language = language
20
+ # print("Use Language:", language)
21
+ self.language_map = load_language_list(language)
22
+
23
+ def __call__(self, key):
24
+ return self.language_map.get(key, key)
25
+
26
+ def print(self):
27
+ # print("Use Language:", self.language)
28
+ print("")
Applio_(Mangio_RVC_Fork).ipynb ADDED
@@ -0,0 +1,169 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": null,
6
+ "metadata": {
7
+ "cellView": "form",
8
+ "id": "izLwNF_8T1TK"
9
+ },
10
+ "outputs": [],
11
+ "source": [
12
+ "#@title <font color='#06ae56'>**🍏 Applio (Mangio-RVC-Fork)**</font>\n",
13
+ "import time\n",
14
+ "import os\n",
15
+ "import subprocess\n",
16
+ "import shutil\n",
17
+ "import threading\n",
18
+ "import base64\n",
19
+ "import threading\n",
20
+ "import time\n",
21
+ "from IPython.display import HTML, clear_output\n",
22
+ "\n",
23
+ "nosv_name1 = base64.b64decode(('ZXh0ZXJuYWxj').encode('ascii')).decode('ascii')\n",
24
+ "nosv_name2 = base64.b64decode(('b2xhYmNvZGU=').encode('ascii')).decode('ascii')\n",
25
+ "guebui = base64.b64decode(('V2U=').encode('ascii')).decode('ascii')\n",
26
+ "guebui2 = base64.b64decode(('YlVJ').encode('ascii')).decode('ascii')\n",
27
+ "pbestm = base64.b64decode(('cm12cGU=').encode('ascii')).decode('ascii')\n",
28
+ "tryre = base64.b64decode(('UmV0cmlldmFs').encode('ascii')).decode('ascii')\n",
29
+ "\n",
30
+ "xdsame = '/content/'+ tryre +'-based-Voice-Conversion-' + guebui + guebui2 +'/'\n",
31
+ "\n",
32
+ "collapsible_section = \"\"\"\n",
33
+ "<br>\n",
34
+ "<br>\n",
35
+ "<details style=\"border: 1px solid #ddd; border-radius: 5px; padding: 10px; margin-bottom: 10px;\">\n",
36
+ " <summary open style=\"font-weight: bold; cursor: pointer;\">🚀 Click to learn more about Applio</summary>\n",
37
+ " <div style=\"margin-left: 20px;\">\n",
38
+ " <ul>\n",
39
+ " <li><a href=\"https://github.com/Mangio621/Mangio-RVC-Fork\" style=\"color: #06ae56;\">Mangio-RVC-Fork</a> - Source of inspiration and base for this improved code, special thanks to the developers.</li>\n",
40
+ " <li><a href=\"https://github.com/Anjok07/ultimatevocalremovergui\" style=\"color: #06ae56;\">UltimateVocalRemover</a> - Used for voice and instrument separation.</li>\n",
41
+ " <li>Vidal, Blaise & Aitron - Contributors to the Applio version.</li>\n",
42
+ " <li>kalomaze - Creator of external scripts that help the functioning of Applio.</li>\n",
43
+ " </ul>\n",
44
+ " <p style=\"color: #fff;\">Join and contribute to the project on <a href=\"https://github.com/IAHispano/Applio-RVC-Fork\" style=\"color: #06ae56;\">our GitHub repository</a>.</p>\n",
45
+ " </div>\n",
46
+ "</details>\n",
47
+ "<br>\n",
48
+ "<button style=\"font-weight: bold; cursor: pointer; background-color: #06ae56; color: white; border: 1px solid #fff; border-radius: 4px; padding: 10px 20px; text-decoration: none;\" onclick=\"window.open('https://discord.gg/IAHispano', '_blank')\">🍏 Join our support Discord server (IA Hispano)</button>\n",
49
+ "<br>\n",
50
+ "<br>\n",
51
+ "\"\"\"\n",
52
+ "#@markdown **Settings:**\n",
53
+ "ForceUpdateDependencies = True\n",
54
+ "ForceNoMountDrive = False\n",
55
+ "#@markdown Restore your backup from Google Drive.\n",
56
+ "LoadBackupDrive = False #@param{type:\"boolean\"}\n",
57
+ "#@markdown Make regular backups of your model's training.\n",
58
+ "AutoBackups = True #@param{type:\"boolean\"}\n",
59
+ "if not os.path.exists(xdsame):\n",
60
+ " current_path = os.getcwd()\n",
61
+ " shutil.rmtree('/content/')\n",
62
+ " os.makedirs('/content/', exist_ok=True)\n",
63
+ "\n",
64
+ " os.chdir(current_path)\n",
65
+ " !git clone https://github.com/IAHispano/$nosv_name1$nosv_name2 /content/$tryre-based-Voice-Conversion-$guebui$guebui2/utils\n",
66
+ " clear_output()\n",
67
+ "\n",
68
+ " os.chdir(xdsame)\n",
69
+ " from utils.dependency import *\n",
70
+ " from utils.clonerepo_experimental import *\n",
71
+ " os.chdir(\"..\")\n",
72
+ "\n",
73
+ "\n",
74
+ "\n",
75
+ " setup_environment(ForceUpdateDependencies, ForceNoMountDrive)\n",
76
+ " clone_repository(True)\n",
77
+ "\n",
78
+ " !wget https://huggingface.co/lj1995/VoiceConversion$guebui$guebui2/resolve/main/rmvpe.pt -P /content/Retrieval-based-Voice-Conversion-$guebui$guebui2/\n",
79
+ " clear_output()\n",
80
+ "\n",
81
+ "base_path = \"/content/Retrieval-based-Voice-Conversion-$guebui$guebui2/\"\n",
82
+ "clear_output()\n",
83
+ "\n",
84
+ "\n",
85
+ "\n",
86
+ "from utils import backups\n",
87
+ "\n",
88
+ "LOGS_FOLDER = xdsame + '/logs'\n",
89
+ "if not os.path.exists(LOGS_FOLDER):\n",
90
+ " os.makedirs(LOGS_FOLDER)\n",
91
+ " clear_output()\n",
92
+ "\n",
93
+ "WEIGHTS_FOLDER = xdsame + '/logs' + '/weights'\n",
94
+ "if not os.path.exists(WEIGHTS_FOLDER):\n",
95
+ " os.makedirs(WEIGHTS_FOLDER)\n",
96
+ " clear_output()\n",
97
+ "\n",
98
+ "others_FOLDER = xdsame + '/audio-others'\n",
99
+ "if not os.path.exists(others_FOLDER):\n",
100
+ " os.makedirs(others_FOLDER)\n",
101
+ " clear_output()\n",
102
+ "\n",
103
+ "audio_outputs_FOLDER = xdsame + '/audio-outputs'\n",
104
+ "if not os.path.exists(audio_outputs_FOLDER):\n",
105
+ " os.makedirs(audio_outputs_FOLDER)\n",
106
+ " clear_output()\n",
107
+ "\n",
108
+ "if LoadBackupDrive:\n",
109
+ " backups.import_google_drive_backup()\n",
110
+ " clear_output()\n",
111
+ "\n",
112
+ "#@markdown Choose the language in which you want the interface to be available.\n",
113
+ "i18n_path = xdsame + 'i18n.py'\n",
114
+ "i18n_new_path = xdsame + 'utils/i18n.py'\n",
115
+ "try:\n",
116
+ " if os.path.exists(i18n_path) and os.path.exists(i18n_new_path):\n",
117
+ " shutil.move(i18n_new_path, i18n_path)\n",
118
+ "\n",
119
+ " SelectedLanguage = \"en_US\" #@param [\"es_ES\", \"en_US\", \"zh_CN\", \"ar_AR\", \"id_ID\", \"pt_PT\", \"ru_RU\", \"ur_UR\", \"tr_TR\", \"it_IT\", \"de_DE\"]\n",
120
+ " new_language_line = ' language = \"' + SelectedLanguage + '\"\\n'\n",
121
+ "#@markdown <a href=\"https://discord.gg/iahispano\"><font>If you need more help, feel free to join our official Discord server!</font></a>\n",
122
+ " with open(i18n_path, 'r') as file:\n",
123
+ " lines = file.readlines()\n",
124
+ "\n",
125
+ " with open(i18n_path, 'w') as file:\n",
126
+ " for index, line in enumerate(lines):\n",
127
+ " if index == 14:\n",
128
+ " file.write(new_language_line)\n",
129
+ " else:\n",
130
+ " file.write(line)\n",
131
+ "\n",
132
+ "except FileNotFoundError:\n",
133
+ " print(\"Translation couldn't be applied successfully. Please restart the environment and run the cell again.\")\n",
134
+ "\n",
135
+ "def start_web_server():\n",
136
+ " %cd /content/$tryre-based-Voice-Conversion-$guebui$guebui2\n",
137
+ " %load_ext tensorboard\n",
138
+ " clear_output()\n",
139
+ " %tensorboard --logdir /content/$tryre-based-Voice-Conversion-$guebui$guebui2/logs\n",
140
+ " !mkdir -p /content/$tryre-based-Voice-Conversion-$guebui$guebui2/audios\n",
141
+ " display(HTML(collapsible_section))\n",
142
+ " !python3 infer-web.py --colab --pycmd python3\n",
143
+ "\n",
144
+ "if AutoBackups:\n",
145
+ " web_server_thread = threading.Thread(target=start_web_server)\n",
146
+ " web_server_thread.start()\n",
147
+ " backups.backup_files()\n",
148
+ "\n",
149
+ "else:\n",
150
+ " start_web_server()"
151
+ ]
152
+ }
153
+ ],
154
+ "metadata": {
155
+ "accelerator": "GPU",
156
+ "colab": {
157
+ "provenance": []
158
+ },
159
+ "kernelspec": {
160
+ "display_name": "Python 3",
161
+ "name": "python3"
162
+ },
163
+ "language_info": {
164
+ "name": "python"
165
+ }
166
+ },
167
+ "nbformat": 4,
168
+ "nbformat_minor": 0
169
+ }
Dockerfile ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # syntax=docker/dockerfile:1
2
+
3
+ FROM python:3.10-bullseye
4
+
5
+ EXPOSE 7865
6
+
7
+ WORKDIR /app
8
+
9
+ COPY . .
10
+
11
+ RUN apt update && apt install -y -qq ffmpeg aria2 && apt clean
12
+
13
+ RUN pip3 install --no-cache-dir -r requirements.txt
14
+
15
+ RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d assets/pretrained_v2/ -o D40k.pth
16
+ RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d assets/pretrained_v2/ -o G40k.pth
17
+ RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth -d assets/pretrained_v2/ -o f0D40k.pth
18
+ RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth -d assets/pretrained_v2/ -o f0G40k.pth
19
+
20
+ RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d assets/uvr5_weights/ -o HP2-人声vocals+非人声instrumentals.pth
21
+ RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d assets/uvr5_weights/ -o HP5-主旋律人声vocals+其他instrumentals.pth
22
+
23
+ RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d assets/hubert -o hubert_base.pt
24
+
25
+ RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt -d assets/hubert -o rmvpe.pt
26
+
27
+ VOLUME [ "/app/weights", "/app/opt" ]
28
+
29
+ CMD ["python3", "infer-web.py"]
Fixes/local_fixes.py ADDED
@@ -0,0 +1,136 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import sys
3
+ import time
4
+ import shutil
5
+ import requests
6
+ import zipfile
7
+
8
+ def insert_new_line(file_name, line_to_find, text_to_insert):
9
+ lines = []
10
+ with open(file_name, 'r', encoding='utf-8') as read_obj:
11
+ lines = read_obj.readlines()
12
+ already_exists = False
13
+ with open(file_name + '.tmp', 'w', encoding='utf-8') as write_obj:
14
+ for i in range(len(lines)):
15
+ write_obj.write(lines[i])
16
+ if lines[i].strip() == line_to_find:
17
+ # If next line exists and starts with sys.path.append, skip
18
+ if i+1 < len(lines) and lines[i+1].strip().startswith("sys.path.append"):
19
+ print('It was already fixed! Skip adding a line...')
20
+ already_exists = True
21
+ break
22
+ else:
23
+ write_obj.write(text_to_insert + '\n')
24
+ # If no existing sys.path.append line was found, replace the original file
25
+ if not already_exists:
26
+ os.replace(file_name + '.tmp', file_name)
27
+ return True
28
+ else:
29
+ # If existing line was found, delete temporary file
30
+ os.remove(file_name + '.tmp')
31
+ return False
32
+
33
+ def replace_in_file(file_name, old_text, new_text):
34
+ with open(file_name, 'r', encoding='utf-8') as file:
35
+ file_contents = file.read()
36
+
37
+ if old_text in file_contents:
38
+ file_contents = file_contents.replace(old_text, new_text)
39
+ with open(file_name, 'w', encoding='utf-8') as file:
40
+ file.write(file_contents)
41
+ return True
42
+
43
+ return False
44
+
45
+ if __name__ == "__main__":
46
+ current_path = os.getcwd()
47
+ file_name = os.path.join(current_path, "infer", "modules", "train", "extract", "extract_f0_print.py")
48
+ line_to_find = 'import numpy as np, logging'
49
+ text_to_insert = "sys.path.append(r'" + current_path + "')"
50
+
51
+
52
+ success_1 = insert_new_line(file_name, line_to_find, text_to_insert)
53
+ if success_1:
54
+ print('The first operation was successful!')
55
+ else:
56
+ print('He skipped the first operation because it was already fixed!')
57
+
58
+ file_name = 'infer-web.py'
59
+ old_text = 'with gr.Blocks(theme=gr.themes.Soft()) as app:'
60
+ new_text = 'with gr.Blocks() as app:'
61
+
62
+ success_2 = replace_in_file(file_name, old_text, new_text)
63
+ if success_2:
64
+ print('The second operation was successful!')
65
+ else:
66
+ print('The second operation was omitted because it was already fixed!')
67
+
68
+ print('Local corrections successful! You should now be able to infer and train locally in Applio RVC Fork.')
69
+
70
+ time.sleep(5)
71
+
72
+ def find_torchcrepe_directory(directory):
73
+ """
74
+ Recursively searches for the topmost folder named 'torchcrepe' within a directory.
75
+ Returns the path of the directory found or None if none is found.
76
+ """
77
+ for root, dirs, files in os.walk(directory):
78
+ if 'torchcrepe' in dirs:
79
+ return os.path.join(root, 'torchcrepe')
80
+ return None
81
+
82
+ def download_and_extract_torchcrepe():
83
+ url = 'https://github.com/maxrmorrison/torchcrepe/archive/refs/heads/master.zip'
84
+ temp_dir = 'temp_torchcrepe'
85
+ destination_dir = os.getcwd()
86
+
87
+ try:
88
+ torchcrepe_dir_path = os.path.join(destination_dir, 'torchcrepe')
89
+
90
+ if os.path.exists(torchcrepe_dir_path):
91
+ print("Skipping the torchcrepe download. The folder already exists.")
92
+ return
93
+
94
+ # Download the file
95
+ print("Starting torchcrepe download...")
96
+ response = requests.get(url)
97
+
98
+ # Raise an error if the GET request was unsuccessful
99
+ response.raise_for_status()
100
+ print("Download completed.")
101
+
102
+ # Save the downloaded file
103
+ zip_file_path = os.path.join(temp_dir, 'master.zip')
104
+ os.makedirs(temp_dir, exist_ok=True)
105
+ with open(zip_file_path, 'wb') as file:
106
+ file.write(response.content)
107
+ print(f"Zip file saved to {zip_file_path}")
108
+
109
+ # Extract the zip file
110
+ print("Extracting content...")
111
+ with zipfile.ZipFile(zip_file_path, 'r') as zip_file:
112
+ zip_file.extractall(temp_dir)
113
+ print("Extraction completed.")
114
+
115
+ # Locate the torchcrepe folder and move it to the destination directory
116
+ torchcrepe_dir = find_torchcrepe_directory(temp_dir)
117
+ if torchcrepe_dir:
118
+ shutil.move(torchcrepe_dir, destination_dir)
119
+ print(f"Moved the torchcrepe directory to {destination_dir}!")
120
+ else:
121
+ print("The torchcrepe directory could not be located.")
122
+
123
+ except Exception as e:
124
+ print("Torchcrepe not successfully downloaded", e)
125
+
126
+ # Clean up temporary directory
127
+ if os.path.exists(temp_dir):
128
+ shutil.rmtree(temp_dir)
129
+
130
+ # Run the function
131
+ download_and_extract_torchcrepe()
132
+
133
+ temp_dir = 'temp_torchcrepe'
134
+
135
+ if os.path.exists(temp_dir):
136
+ shutil.rmtree(temp_dir)
Fixes/tensor-launch.py ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import threading
2
+ import time
3
+ from tensorboard import program
4
+ import os
5
+
6
+ log_path = "logs"
7
+
8
+ if __name__ == "__main__":
9
+ tb = program.TensorBoard()
10
+ tb.configure(argv=[None, '--logdir', log_path])
11
+ url = tb.launch()
12
+ print(f'Tensorboard can be accessed at: {url}')
13
+
14
+ while True:
15
+ time.sleep(600) # Keep the main thread running
LICENSE ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ MIT License
2
+
3
+ Copyright (c) 2023 liujing04
4
+ Copyright (c) 2023 源文雨
5
+
6
+ Permission is hereby granted, free of charge, to any person obtaining a copy
7
+ of this software and associated documentation files (the "Software"), to deal
8
+ in the Software without restriction, including without limitation the rights
9
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
10
+ copies of the Software, and to permit persons to whom the Software is
11
+ furnished to do so, subject to the following conditions:
12
+
13
+ The above copyright notice and this permission notice shall be included in all
14
+ copies or substantial portions of the Software.
15
+
16
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22
+ SOFTWARE.
23
+
24
+ The licenses for related libraries are as follows:
25
+
26
+ ContentVec
27
+ https://github.com/auspicious3000/contentvec/blob/main/LICENSE
28
+ MIT License
29
+
30
+ VITS
31
+ https://github.com/jaywalnut310/vits/blob/main/LICENSE
32
+ MIT License
33
+
34
+ HIFIGAN
35
+ https://github.com/jik876/hifi-gan/blob/master/LICENSE
36
+ MIT License
37
+
38
+ gradio
39
+ https://github.com/gradio-app/gradio/blob/main/LICENSE
40
+ Apache License 2.0
41
+
42
+ ffmpeg
43
+ https://github.com/FFmpeg/FFmpeg/blob/master/COPYING.LGPLv3
44
+ https://github.com/BtbN/FFmpeg-Builds/releases/download/autobuild-2021-02-28-12-32/ffmpeg-n4.3.2-160-gfbb9368226-win64-lgpl-4.3.zip
45
+ LPGLv3 License
46
+ MIT License
47
+
48
+ ultimatevocalremovergui
49
+ https://github.com/Anjok07/ultimatevocalremovergui/blob/master/LICENSE
50
+ https://github.com/yang123qwe/vocal_separation_by_uvr5
51
+ MIT License
52
+
53
+ audio-slicer
54
+ https://github.com/openvpi/audio-slicer/blob/main/LICENSE
55
+ MIT License
56
+
57
+ PySimpleGUI
58
+ https://github.com/PySimpleGUI/PySimpleGUI/blob/master/license.txt
59
+ LPGLv3 License
LazyImport.py ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from importlib.util import find_spec, LazyLoader, module_from_spec
2
+ from sys import modules
3
+
4
+ def lazyload(name):
5
+ if name in modules:
6
+ return modules[name]
7
+ else:
8
+ spec = find_spec(name)
9
+ loader = LazyLoader(spec.loader)
10
+ module = module_from_spec(spec)
11
+ modules[name] = module
12
+ loader.exec_module(module)
13
+ return module
MDX-Net_Colab.ipynb ADDED
@@ -0,0 +1,524 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "metadata": {
6
+ "id": "wX9xzLur4tus"
7
+ },
8
+ "source": [
9
+ "# MDX-Net Colab\n",
10
+ "<div style=\"display:flex; align-items:center; font-size: 16px;\">\n",
11
+ " <img src=\"https://github.githubassets.com/pinned-octocat.svg\" alt=\"icon1\" style=\"margin-right:10px; height: 20px;\" width=\"1.5%\">\n",
12
+ " <span>Trained models provided in this notebook are from <a href=\"https://github.com/Anjok07\">UVR-GUI</a>.</span>\n",
13
+ "</div>\n",
14
+ "<div style=\"display:flex; align-items:center; font-size: 16px;\">\n",
15
+ " <img src=\"https://github.com/Anjok07/ultimatevocalremovergui/raw/master/gui_data/img/GUI-Icon.ico\" alt=\"icon2\" style=\"margin-right:10px; height: 20px;margin-top:10px\" width=\"1.5%\">\n",
16
+ " <span>OFFICIAL UVR GITHUB PAGE: <a href=\"https://github.com/Anjok07/ultimatevocalremovergui\">here</a>.</span>\n",
17
+ "</div>\n",
18
+ "<div style=\"display:flex; align-items:center; font-size: 16px;\">\n",
19
+ " <img src=\"https://avatars.githubusercontent.com/u/24620594\" alt=\"icon3\" style=\"margin-right:10px; height: 20px;\" width=\"1.5%\">\n",
20
+ " <span>OFFICIAL CLI Version: <a href=\"https://github.com/tsurumeso/vocal-remover\">here</a>.</span>\n",
21
+ "</div>\n",
22
+ "<div style=\"display:flex; align-items:center; font-size: 16px;\">\n",
23
+ " <img src=\"https://icons.getbootstrap.com/assets/icons/discord.svg\" alt=\"icon4\" style=\"margin-right:10px; height: 20px;\" width=\"1.5%\">\n",
24
+ " <span>Join our <a href=\"https://cutt.ly/0TcDjmo\">Discord server</a>!</span>\n",
25
+ "</div>\n",
26
+ "<sup><br>Ultimate Vocal Remover (unofficial)</sup>\n",
27
+ "<sup><br>MDX-Net by <a href=\"https://github.com/kuielab\">kuielab</a> and adapted for Colaboratory by <a href=\"https://www.youtube.com/channel/UC0NiSV1jLMH-9E09wiDVFYw\">AudioHacker</a>.</sup>\n",
28
+ "\n",
29
+ "<sup><br>Your support means a lot to me. If you enjoy my work, please consider buying me a ko-fi:<br></sup>\n",
30
+ "[![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/X8X6M8FR0)"
31
+ ]
32
+ },
33
+ {
34
+ "cell_type": "code",
35
+ "execution_count": null,
36
+ "metadata": {
37
+ "id": "3J69RV7G8ocb",
38
+ "cellView": "form"
39
+ },
40
+ "outputs": [],
41
+ "source": [
42
+ "import json\n",
43
+ "import os\n",
44
+ "import os.path\n",
45
+ "import gc\n",
46
+ "import psutil\n",
47
+ "import requests\n",
48
+ "import subprocess\n",
49
+ "import glob\n",
50
+ "import time\n",
51
+ "import logging\n",
52
+ "import sys\n",
53
+ "from bs4 import BeautifulSoup\n",
54
+ "from google.colab import drive, files, output\n",
55
+ "from IPython.display import Audio, display\n",
56
+ "\n",
57
+ "if \"first_cell_ran\" in locals():\n",
58
+ " print(\"You've ran this cell for this session. No need to run it again.\\nif you think something went wrong or you want to change mounting path, restart the runtime.\")\n",
59
+ "else:\n",
60
+ " print('Setting up... please wait around 1-2 minute(s).')\n",
61
+ "\n",
62
+ " branch = \"https://github.com/NaJeongMo/Colab-for-MDX_B\"\n",
63
+ "\n",
64
+ " model_params = \"https://raw.githubusercontent.com/TRvlvr/application_data/main/mdx_model_data/model_data.json\"\n",
65
+ " _Models = \"https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/\"\n",
66
+ " # _models = \"https://pastebin.com/raw/jBzYB8vz\"\n",
67
+ " _models = \"https://raw.githubusercontent.com/TRvlvr/application_data/main/filelists/download_checks.json\"\n",
68
+ " stem_naming = \"https://pastebin.com/raw/mpH4hRcF\"\n",
69
+ " arl_check_endpoint = 'https://dz.doubledouble.top/check' # param: arl?=<>\n",
70
+ "\n",
71
+ " file_folder = \"Colab-for-MDX_B\"\n",
72
+ "\n",
73
+ " model_ids = requests.get(_models).json()\n",
74
+ " model_ids = model_ids[\"mdx_download_list\"].values()\n",
75
+ "\n",
76
+ " model_params = requests.get(model_params).json()\n",
77
+ " stem_naming = requests.get(stem_naming).json()\n",
78
+ "\n",
79
+ " os.makedirs(\"tmp_models\", exist_ok=True)\n",
80
+ "\n",
81
+ " # @markdown If you don't wish to mount google drive, uncheck this box.\n",
82
+ " MountDrive = True # @param{type:\"boolean\"}\n",
83
+ " # @markdown The path for the drive to be mounted: Please be cautious when modifying this as it can cause issues if not done properly.\n",
84
+ " mounting_path = \"/content/drive/MyDrive\" # @param [\"snippets:\",\"/content/drive/MyDrive\",\"/content/drive/Shareddrives/<your shared drive name>\", \"/content/drive/Shareddrives/Shared Drive\"]{allow-input: true}\n",
85
+ " # @markdown Force update and disregard local changes: discards all local modifications in your repository, effectively replacing all files with the versions from the original commit.\n",
86
+ " force_update = False # @param{type:\"boolean\"}\n",
87
+ " # @markdown Auto Update (does not discard your changes)\n",
88
+ " auto_update = True # @param{type:\"boolean\"}\n",
89
+ "\n",
90
+ "\n",
91
+ " reqs_apt = [] # !sudo apt-get install\n",
92
+ " reqs_pip = [\"librosa>=0.6.3,<0.9\", \"onnxruntime_gpu\", \"deemix\", \"yt_dlp\"] # pip3 install\n",
93
+ "\n",
94
+ " class hide_opt: # hide outputs\n",
95
+ " def __enter__(self):\n",
96
+ " self._original_stdout = sys.stdout\n",
97
+ " sys.stdout = open(os.devnull, \"w\")\n",
98
+ "\n",
99
+ " def __exit__(self, exc_type, exc_val, exc_tb):\n",
100
+ " sys.stdout.close()\n",
101
+ " sys.stdout = self._original_stdout\n",
102
+ "\n",
103
+ " def get_size(bytes, suffix=\"B\"): # read ram\n",
104
+ " global svmem\n",
105
+ " factor = 1024\n",
106
+ " for unit in [\"\", \"K\", \"M\", \"G\", \"T\", \"P\"]:\n",
107
+ " if bytes < factor:\n",
108
+ " return f\"{bytes:.2f}{unit}{suffix}\"\n",
109
+ " bytes /= factor\n",
110
+ " svmem = psutil.virtual_memory()\n",
111
+ "\n",
112
+ "\n",
113
+ " print('installing requirements...',end=' ')\n",
114
+ " with hide_opt():\n",
115
+ " for x in reqs_apt:\n",
116
+ " subprocess.run([\"sudo\", \"apt-get\", \"install\", x])\n",
117
+ " for x in reqs_pip:\n",
118
+ " subprocess.run([\"python3\", \"-m\", \"pip\", \"install\", x])\n",
119
+ " print('done')\n",
120
+ "\n",
121
+ " def install_or_mount_drive():\n",
122
+ " print(\n",
123
+ " \"Please log in to your account by following the prompts in the pop-up tab.\\nThis step is necessary to install the files to your Google Drive.\\nIf you have any concerns about the safety of this notebook, you can choose not to mount your drive by unchecking the \\\"MountDrive\\\" checkbox.\"\n",
124
+ " )\n",
125
+ " drive.mount(\"/content/drive\", force_remount=True)\n",
126
+ " os.chdir(mounting_path)\n",
127
+ " # check if previous installation is done\n",
128
+ " if os.path.exists(os.path.join(mounting_path, file_folder)):\n",
129
+ " # update checking\n",
130
+ " os.chdir(file_folder)\n",
131
+ "\n",
132
+ " if force_update:\n",
133
+ " print('Force updating...')\n",
134
+ "\n",
135
+ " commands = [\n",
136
+ " [\"git\", \"pull\"],\n",
137
+ " [\"git\", \"checkout\", \"--\", \".\"],\n",
138
+ " ]\n",
139
+ "\n",
140
+ " for cmd in commands:\n",
141
+ " subprocess.run(cmd)\n",
142
+ "\n",
143
+ " elif auto_update:\n",
144
+ " print('Checking for updates...')\n",
145
+ " commands = [\n",
146
+ " [\"git\", \"pull\"],\n",
147
+ " ]\n",
148
+ "\n",
149
+ " for cmd in commands:\n",
150
+ " subprocess.run(cmd)\n",
151
+ " else:\n",
152
+ " subprocess.run([\"git\", \"clone\", \"https://github.com/NaJeongMo/Colab-for-MDX_B.git\"])\n",
153
+ " os.chdir(file_folder)\n",
154
+ "\n",
155
+ " def use_uvr_without_saving():\n",
156
+ " global mounting_path\n",
157
+ " print(\"Notice: files won't be saved to personal drive.\")\n",
158
+ " print(f\"Downloading {file_folder}...\", end=\" \")\n",
159
+ " mounting_path = \"/content\"\n",
160
+ " with hide_opt():\n",
161
+ " os.chdir(mounting_path)\n",
162
+ " subprocess.run([\"git\", \"clone\", \"https://github.com/NaJeongMo/Colab-for-MDX_B.git\"])\n",
163
+ " os.chdir(file_folder)\n",
164
+ "\n",
165
+ " if MountDrive:\n",
166
+ " install_or_mount_drive()\n",
167
+ " else:\n",
168
+ " use_uvr_without_saving()\n",
169
+ " print(\"done!\")\n",
170
+ " if not os.path.exists(\"tracks\"):\n",
171
+ " os.mkdir(\"tracks\")\n",
172
+ "\n",
173
+ " print('Importing required libraries...',end=' ')\n",
174
+ "\n",
175
+ " import os\n",
176
+ " import mdx\n",
177
+ " import librosa\n",
178
+ " import torch\n",
179
+ " import soundfile as sf\n",
180
+ " import numpy as np\n",
181
+ " import yt_dlp\n",
182
+ "\n",
183
+ " from deezer import Deezer\n",
184
+ " from deezer import TrackFormats\n",
185
+ " import deemix\n",
186
+ " from deemix.settings import load as loadSettings\n",
187
+ " from deemix.downloader import Downloader\n",
188
+ " from deemix import generateDownloadObject\n",
189
+ "\n",
190
+ " logger = logging.getLogger(\"yt_dlp\")\n",
191
+ " logger.setLevel(logging.ERROR)\n",
192
+ "\n",
193
+ " def id_to_ptm(mkey):\n",
194
+ " if mkey in model_ids:\n",
195
+ " mpath = f\"/content/tmp_models/{mkey}\"\n",
196
+ " if not os.path.exists(f'/content/tmp_models/{mkey}'):\n",
197
+ " print('Downloading model...',end=' ')\n",
198
+ " subprocess.run(\n",
199
+ " [\"wget\", _Models+mkey, \"-O\", mpath]\n",
200
+ " )\n",
201
+ " print(f'saved to {mpath}')\n",
202
+ " # get_ipython().system(f'gdown {model_id} -O /content/tmp_models/{mkey}')\n",
203
+ " return mpath\n",
204
+ " else:\n",
205
+ " return mpath\n",
206
+ " else:\n",
207
+ " mpath = f'models/{mkey}'\n",
208
+ " return mpath\n",
209
+ "\n",
210
+ " def prepare_mdx(custom_param=False, dim_f=None, dim_t=None, n_fft=None, stem_name=None, compensation=None):\n",
211
+ " device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')\n",
212
+ " if custom_param:\n",
213
+ " assert not (dim_f is None or dim_t is None or n_fft is None or compensation is None), 'Custom parameter selected, but incomplete parameters are provided.'\n",
214
+ " mdx_model = mdx.MDX_Model(\n",
215
+ " device,\n",
216
+ " dim_f = dim_f,\n",
217
+ " dim_t = dim_t,\n",
218
+ " n_fft = n_fft,\n",
219
+ " stem_name=stem_name,\n",
220
+ " compensation=compensation\n",
221
+ " )\n",
222
+ " else:\n",
223
+ " model_hash = mdx.MDX.get_hash(onnx)\n",
224
+ " if model_hash in model_params:\n",
225
+ " mp = model_params.get(model_hash)\n",
226
+ " mdx_model = mdx.MDX_Model(\n",
227
+ " device,\n",
228
+ " dim_f = mp[\"mdx_dim_f_set\"],\n",
229
+ " dim_t = 2**mp[\"mdx_dim_t_set\"],\n",
230
+ " n_fft = mp[\"mdx_n_fft_scale_set\"],\n",
231
+ " stem_name=mp[\"primary_stem\"],\n",
232
+ " compensation=compensation if not custom_param and compensation is not None else mp[\"compensate\"]\n",
233
+ " )\n",
234
+ " return mdx_model\n",
235
+ "\n",
236
+ " def run_mdx(onnx, mdx_model,filename,diff=False,suffix=None,diff_suffix=None, denoise=False, m_threads=1):\n",
237
+ " mdx_sess = mdx.MDX(onnx,mdx_model)\n",
238
+ " print(f\"Processing: {filename}\")\n",
239
+ " wave, sr = librosa.load(filename,mono=False, sr=44100)\n",
240
+ " # normalizing input wave gives better output\n",
241
+ " peak = max(np.max(wave), abs(np.min(wave)))\n",
242
+ " wave /= peak\n",
243
+ " if denoise:\n",
244
+ " wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (mdx_sess.process_wave(wave, m_threads))\n",
245
+ " wave_processed *= 0.5\n",
246
+ " else:\n",
247
+ " wave_processed = mdx_sess.process_wave(wave, m_threads)\n",
248
+ " # return to previous peak\n",
249
+ " wave_processed *= peak\n",
250
+ "\n",
251
+ " stem_name = mdx_model.stem_name if suffix is None else suffix # use suffix if provided\n",
252
+ " save_path = f\"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav\"\n",
253
+ " save_path = os.path.join(\n",
254
+ " 'separated',\n",
255
+ " save_path\n",
256
+ " )\n",
257
+ " sf.write(\n",
258
+ " save_path,\n",
259
+ " wave_processed.T,\n",
260
+ " sr\n",
261
+ " )\n",
262
+ "\n",
263
+ " print(f'done, saved to: {save_path}')\n",
264
+ "\n",
265
+ " if diff:\n",
266
+ " diff_stem_name = stem_naming.get(stem_name) if diff_suffix is None else diff_suffix # use suffix if provided\n",
267
+ " stem_name = f\"{stem_name}_diff\" if diff_stem_name is None else diff_stem_name\n",
268
+ " save_path = f\"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav\"\n",
269
+ " save_path = os.path.join(\n",
270
+ " 'separated',\n",
271
+ " save_path\n",
272
+ " )\n",
273
+ " sf.write(\n",
274
+ " save_path,\n",
275
+ " (-wave_processed.T*mdx_model.compensation)+wave.T,\n",
276
+ " sr\n",
277
+ " )\n",
278
+ " print(f'invert done, saved to: {save_path}')\n",
279
+ " del mdx_sess, wave_processed, wave\n",
280
+ " gc.collect()\n",
281
+ "\n",
282
+ " def is_valid_url(url):\n",
283
+ " import re\n",
284
+ " regex = re.compile(\n",
285
+ " r'^https?://'\n",
286
+ " r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\\.)+[A-Z]{2,6}\\.?|'\n",
287
+ " r'localhost|'\n",
288
+ " r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})'\n",
289
+ " r'(?::\\d+)?'\n",
290
+ " r'(?:/?|[/?]\\S+)$', re.IGNORECASE)\n",
291
+ " return url is not None and regex.search(url)\n",
292
+ "\n",
293
+ " def download_deezer(link, arl, fmt='FLAC'):\n",
294
+ " match fmt:\n",
295
+ " case 'FLAC':\n",
296
+ " bitrate = TrackFormats.FLAC\n",
297
+ " case 'MP3_320':\n",
298
+ " bitrate = TrackFormats.MP3_320\n",
299
+ " case 'MP3_128':\n",
300
+ " bitrate = TrackFormats.MP3_128\n",
301
+ " case _:\n",
302
+ " bitrate = TrackFormats.MP3_128\n",
303
+ "\n",
304
+ " dz = Deezer()\n",
305
+ " settings = loadSettings('dz_config')\n",
306
+ " settings['downloadLocation'] = './tracks'\n",
307
+ " if not dz.login_via_arl(arl.strip()):\n",
308
+ " raise Exception('Error while logging in with provided ARL.')\n",
309
+ " downloadObject = generateDownloadObject(dz, link, bitrate)\n",
310
+ " print(f'Downloading {downloadObject.type}: \"{downloadObject.title}\" by {downloadObject.artist}...',end=' ',flush=True)\n",
311
+ " Downloader(dz, downloadObject, settings).start()\n",
312
+ " print(f'done.')\n",
313
+ "\n",
314
+ " path_to_audio = []\n",
315
+ " for file in downloadObject.files:\n",
316
+ " path_to_audio.append(file[\"path\"])\n",
317
+ "\n",
318
+ " return path_to_audio\n",
319
+ "\n",
320
+ " def download_link(url):\n",
321
+ " ydl_opts = {\n",
322
+ " 'format': 'bestvideo+bestaudio/best',\n",
323
+ " 'outtmpl': '%(title)s.%(ext)s',\n",
324
+ " 'nocheckcertificate': True,\n",
325
+ " 'ignoreerrors': True,\n",
326
+ " 'no_warnings': True,\n",
327
+ " 'extractaudio': True,\n",
328
+ " }\n",
329
+ " with yt_dlp.YoutubeDL(ydl_opts) as ydl:\n",
330
+ " result = ydl.extract_info(url, download=True)\n",
331
+ " download_path = ydl.prepare_filename(result)\n",
332
+ " return download_path\n",
333
+ "\n",
334
+ " print('finished setting up!')\n",
335
+ " first_cell_ran = True"
336
+ ]
337
+ },
338
+ {
339
+ "cell_type": "code",
340
+ "execution_count": null,
341
+ "metadata": {
342
+ "id": "4hd1TzEGCiRo",
343
+ "cellView": "form"
344
+ },
345
+ "outputs": [],
346
+ "source": [
347
+ "if 'first_cell_ran' in locals():\n",
348
+ " os.chdir(mounting_path + '/' + file_folder + '/')\n",
349
+ " #parameter markdowns-----------------\n",
350
+ " #@markdown ### Input files\n",
351
+ " #@markdown track filename: Upload your songs to the \"tracks\" folder. You may provide multiple links/files by spliting them with ;\n",
352
+ " filename = \"https://deezer.com/album/281108671\" #@param {type:\"string\"}\n",
353
+ " #@markdown onnx model (if you have your own model, upload it in models folder)\n",
354
+ " onnx = \"UVR-MDX-NET-Inst_HQ_3.onnx\" #@param [\"Kim_Inst.onnx\", \"Kim_Vocal_1.onnx\", \"Kim_Vocal_2.onnx\", \"kuielab_a_bass.onnx\", \"kuielab_a_drums.onnx\", \"kuielab_a_other.onnx\", \"kuielab_a_vocals.onnx\", \"kuielab_b_bass.onnx\", \"kuielab_b_drums.onnx\", \"kuielab_b_other.onnx\", \"kuielab_b_vocals.onnx\", \"Reverb_HQ_By_FoxJoy.onnx\", \"UVR-MDX-NET-Inst_1.onnx\", \"UVR-MDX-NET-Inst_2.onnx\", \"UVR-MDX-NET-Inst_3.onnx\", \"UVR-MDX-NET-Inst_HQ_1.onnx\", \"UVR-MDX-NET-Inst_HQ_2.onnx\", \"UVR-MDX-NET-Inst_Main.onnx\", \"UVR_MDXNET_1_9703.onnx\", \"UVR_MDXNET_2_9682.onnx\", \"UVR_MDXNET_3_9662.onnx\", \"UVR_MDXNET_9482.onnx\", \"UVR_MDXNET_KARA.onnx\", \"UVR_MDXNET_KARA_2.onnx\", \"UVR_MDXNET_Main.onnx\", \"UVR-MDX-NET-Inst_HQ_3.onnx\", \"UVR-MDX-NET-Voc_FT.onnx\"]{allow-input: true}\n",
355
+ " #@markdown process all: processes all tracks inside tracks/ folder instead. (filename will be ignored!)\n",
356
+ " process_all = False # @param{type:\"boolean\"}\n",
357
+ "\n",
358
+ "\n",
359
+ " #@markdown ### Settings\n",
360
+ " #@markdown invert: get difference between input and output (e.g get Instrumental out of Vocals)\n",
361
+ " invert = True # @param{type:\"boolean\"}\n",
362
+ " #@markdown denoise: get rid of MDX noise. (This processes input track twice)\n",
363
+ " denoise = True # @param{type:\"boolean\"}\n",
364
+ " #@markdown m_threads: like batch size, processes input wave in n threads. (beneficial for CPU)\n",
365
+ " m_threads = 2 #@param {type:\"slider\", min:1, max:8, step:1}\n",
366
+ "\n",
367
+ " #@markdown ### Custom model parameters (Only use this if you're using new/unofficial/custom models)\n",
368
+ " #@markdown Use custom model parameters. (Default: unchecked, or auto)\n",
369
+ " use_custom_parameter = False # @param{type:\"boolean\"}\n",
370
+ " #@markdown Output file suffix (usually the stem name e.g Vocals)\n",
371
+ " suffix = \"Vocals_custom\" #@param [\"Vocals\", \"Drums\", \"Bass\", \"Other\"]{allow-input: true}\n",
372
+ " suffix_invert = \"Instrumental_custom\" #@param [\"Instrumental\", \"Drumless\", \"Bassless\", \"Instruments\"]{allow-input: true}\n",
373
+ " #@markdown Model parameters\n",
374
+ " dim_f = 3072 #@param {type: \"integer\"}\n",
375
+ " dim_t = 256 #@param {type: \"integer\"}\n",
376
+ " n_fft = 6144 #@param {type: \"integer\"}\n",
377
+ " #@markdown use custom compensation: only if you have your own compensation value for your model. this still apply even if you don't have use_custom_parameter checked (Default: unchecked, or auto)\n",
378
+ " use_custom_compensation = False # @param{type:\"boolean\"}\n",
379
+ " compensation = 1.000 #@param {type: \"number\"}\n",
380
+ "\n",
381
+ " #@markdown ### Extras\n",
382
+ " #@markdown Deezer arl: paste your ARL here for deezer tracks directly!\n",
383
+ " arl = \"\" #@param {type:\"string\"}\n",
384
+ " #@markdown Track format: select track quality/format\n",
385
+ " track_format = \"FLAC\" #@param [\"FLAC\",\"MP3_320\",\"MP3_128\"]\n",
386
+ " #@markdown Print settings being used in the run\n",
387
+ " print_settings = True # @param{type:\"boolean\"}\n",
388
+ "\n",
389
+ "\n",
390
+ "\n",
391
+ " onnx = id_to_ptm(onnx)\n",
392
+ " compensation = compensation if use_custom_compensation or use_custom_parameter else None\n",
393
+ " mdx_model = prepare_mdx(use_custom_parameter, dim_f, dim_t, n_fft, compensation=compensation)\n",
394
+ "\n",
395
+ " filename_split = filename.split(';')\n",
396
+ "\n",
397
+ " usable_files = []\n",
398
+ "\n",
399
+ " if not process_all:\n",
400
+ " for fn in filename_split:\n",
401
+ " fn = fn.strip()\n",
402
+ " if is_valid_url(fn):\n",
403
+ " dm, ltype, lid = deemix.parseLink(fn)\n",
404
+ " if ltype and lid:\n",
405
+ " usable_files += download_deezer(fn, arl, track_format)\n",
406
+ " else:\n",
407
+ " print('downloading link...',end=' ')\n",
408
+ " usable_files+=[download_link(fn)]\n",
409
+ " print('done')\n",
410
+ " else:\n",
411
+ " usable_files.append(os.path.join('tracks',fn))\n",
412
+ " else:\n",
413
+ " for fn in glob.glob('tracks/*'):\n",
414
+ " usable_files.append(fn)\n",
415
+ " for filename in usable_files:\n",
416
+ " suffix_naming = suffix if use_custom_parameter else None\n",
417
+ " diff_suffix_naming = suffix_invert if use_custom_parameter else None\n",
418
+ " run_mdx(onnx, mdx_model, filename, diff=invert,suffix=suffix_naming,diff_suffix=diff_suffix_naming,denoise=denoise)\n",
419
+ "\n",
420
+ " if print_settings:\n",
421
+ " print()\n",
422
+ " print('[MDX-Net_Colab settings used]')\n",
423
+ " print(f'Model used: {onnx}')\n",
424
+ " print(f'Model MD5: {mdx.MDX.get_hash(onnx)}')\n",
425
+ " print(f'Using de-noise: {denoise}')\n",
426
+ " print(f'Model parameters:')\n",
427
+ " print(f' -dim_f: {mdx_model.dim_f}')\n",
428
+ " print(f' -dim_t: {mdx_model.dim_t}')\n",
429
+ " print(f' -n_fft: {mdx_model.n_fft}')\n",
430
+ " print(f' -compensation: {mdx_model.compensation}')\n",
431
+ " print()\n",
432
+ " print('[Input file]')\n",
433
+ " print('filename(s): ')\n",
434
+ " for filename in usable_files:\n",
435
+ " print(f' -{filename}')\n",
436
+ "\n",
437
+ " del mdx_model"
438
+ ]
439
+ },
440
+ {
441
+ "cell_type": "markdown",
442
+ "source": [
443
+ "# Guide\n",
444
+ "\n",
445
+ "This tutorial guide will walk you through the steps to use the features of this Colab notebook.\n",
446
+ "\n",
447
+ "## Mount Drive\n",
448
+ "\n",
449
+ "To mount your Google Drive, follow these steps:\n",
450
+ "\n",
451
+ "1. Check the box next to \"MountDrive\" if you want to mount Google Drive.\n",
452
+ "2. Modify the \"mounting_path\" if you want to specify a different path for the drive to be mounted. **Note:** Be cautious when modifying this path as it can cause issues if not done properly.\n",
453
+ "3. Check the box next to \"Force update and disregard local changes\" if you want to discard all local modifications in your repository and replace the files with the versions from the original commit.\n",
454
+ "4. Check the box next to \"Auto Update\" if you want to automatically update without discarding your changes. Leave it unchecked if you want to manually update.\n",
455
+ "\n",
456
+ "## Input Files\n",
457
+ "\n",
458
+ "To upload your songs, follow these steps:\n",
459
+ "\n",
460
+ "1. Specify the \"track filename\" for your songs. You can provide multiple links or files by separating them with a semicolon (;).\n",
461
+ "2. Upload your songs to the \"tracks\" folder.\n",
462
+ "\n",
463
+ "## ONNX Model\n",
464
+ "\n",
465
+ "If you have your own ONNX model, follow these steps:\n",
466
+ "\n",
467
+ "1. Upload your model to the \"models\" folder.\n",
468
+ "2. Specify the \"onnx\" filename for your model.\n",
469
+ "\n",
470
+ "## Processing\n",
471
+ "\n",
472
+ "To process your tracks, follow these steps:\n",
473
+ "\n",
474
+ "1. If you want to process all tracks inside the \"tracks\" folder, check the box next to \"process_all\" and ignore the \"filename\" field.\n",
475
+ "2. Specify any additional settings you want:\n",
476
+ " - Check the box next to \"invert\" to get the difference between input and output (e.g., get Instrumental out of Vocals).\n",
477
+ " - Check the box next to \"denoise\" to get rid of MDX noise. This processes the input track twice.\n",
478
+ " - Specify custom model parameters only if you're using new/unofficial/custom models. Use the \"use_custom_parameter\" checkbox to enable this feature.\n",
479
+ " - Specify the output file suffix, which is usually the stem name (e.g., Vocals). Use the \"suffix\" field to specify the suffix for normal processing and the \"suffix_invert\" field for inverted processing.\n",
480
+ "\n",
481
+ "## Model Parameters\n",
482
+ "\n",
483
+ "Specify the following custom model parameters if applicable:\n",
484
+ "\n",
485
+ "- \"dim_f\": The value for the `dim_f` parameter.\n",
486
+ "- \"dim_t\": The value for the `dim_t` parameter.\n",
487
+ "- \"n_fft\": The value for the `n_fft` parameter.\n",
488
+ "- Check the box next to \"use_custom_compensation\" if you have your own compensation value for your model. Specify the compensation value in the \"compensation\" field.\n",
489
+ "\n",
490
+ "## Extras\n",
491
+ "\n",
492
+ "If you're working with Deezer tracks, paste your ARL (Authentication Request Library) in the \"arl\" field to directly access the tracks.\n",
493
+ "\n",
494
+ "Specify the \"Track format\" by selecting the desired quality/format for the track.\n",
495
+ "\n",
496
+ "To print the settings being used in the run, check the box next to \"print_settings\".\n",
497
+ "\n",
498
+ "That's it! You're now ready to use this Colab notebook. Enjoy!\n",
499
+ "\n",
500
+ "## For more detailed guide, proceed to this <a href=\"https://docs.google.com/document/d/17fjNvJzj8ZGSer7c7OFe_CNfUKbAxEh_OBv94ZdRG5c\">link</a>.\n",
501
+ "credits: (discord) deton24"
502
+ ],
503
+ "metadata": {
504
+ "id": "tMVwX5RhZSRP"
505
+ }
506
+ }
507
+ ],
508
+ "metadata": {
509
+ "accelerator": "GPU",
510
+ "colab": {
511
+ "gpuType": "T4",
512
+ "provenance": []
513
+ },
514
+ "kernelspec": {
515
+ "display_name": "Python 3",
516
+ "name": "python3"
517
+ },
518
+ "language_info": {
519
+ "name": "python"
520
+ }
521
+ },
522
+ "nbformat": 4,
523
+ "nbformat_minor": 0
524
+ }
MDXNet.py ADDED
@@ -0,0 +1,272 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import soundfile as sf
2
+ import torch, pdb, os, warnings, librosa
3
+ import numpy as np
4
+ import onnxruntime as ort
5
+ from tqdm import tqdm
6
+ import torch
7
+
8
+ dim_c = 4
9
+
10
+
11
+ class Conv_TDF_net_trim:
12
+ def __init__(
13
+ self, device, model_name, target_name, L, dim_f, dim_t, n_fft, hop=1024
14
+ ):
15
+ super(Conv_TDF_net_trim, self).__init__()
16
+
17
+ self.dim_f = dim_f
18
+ self.dim_t = 2**dim_t
19
+ self.n_fft = n_fft
20
+ self.hop = hop
21
+ self.n_bins = self.n_fft // 2 + 1
22
+ self.chunk_size = hop * (self.dim_t - 1)
23
+ self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(
24
+ device
25
+ )
26
+ self.target_name = target_name
27
+ self.blender = "blender" in model_name
28
+
29
+ out_c = dim_c * 4 if target_name == "*" else dim_c
30
+ self.freq_pad = torch.zeros(
31
+ [1, out_c, self.n_bins - self.dim_f, self.dim_t]
32
+ ).to(device)
33
+
34
+ self.n = L // 2
35
+
36
+ def stft(self, x):
37
+ x = x.reshape([-1, self.chunk_size])
38
+ x = torch.stft(
39
+ x,
40
+ n_fft=self.n_fft,
41
+ hop_length=self.hop,
42
+ window=self.window,
43
+ center=True,
44
+ return_complex=True,
45
+ )
46
+ x = torch.view_as_real(x)
47
+ x = x.permute([0, 3, 1, 2])
48
+ x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape(
49
+ [-1, dim_c, self.n_bins, self.dim_t]
50
+ )
51
+ return x[:, :, : self.dim_f]
52
+
53
+ def istft(self, x, freq_pad=None):
54
+ freq_pad = (
55
+ self.freq_pad.repeat([x.shape[0], 1, 1, 1])
56
+ if freq_pad is None
57
+ else freq_pad
58
+ )
59
+ x = torch.cat([x, freq_pad], -2)
60
+ c = 4 * 2 if self.target_name == "*" else 2
61
+ x = x.reshape([-1, c, 2, self.n_bins, self.dim_t]).reshape(
62
+ [-1, 2, self.n_bins, self.dim_t]
63
+ )
64
+ x = x.permute([0, 2, 3, 1])
65
+ x = x.contiguous()
66
+ x = torch.view_as_complex(x)
67
+ x = torch.istft(
68
+ x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True
69
+ )
70
+ return x.reshape([-1, c, self.chunk_size])
71
+
72
+
73
+ def get_models(device, dim_f, dim_t, n_fft):
74
+ return Conv_TDF_net_trim(
75
+ device=device,
76
+ model_name="Conv-TDF",
77
+ target_name="vocals",
78
+ L=11,
79
+ dim_f=dim_f,
80
+ dim_t=dim_t,
81
+ n_fft=n_fft,
82
+ )
83
+
84
+
85
+ warnings.filterwarnings("ignore")
86
+ cpu = torch.device("cpu")
87
+ if torch.cuda.is_available():
88
+ device = torch.device("cuda:0")
89
+ elif torch.backends.mps.is_available():
90
+ device = torch.device("mps")
91
+ else:
92
+ device = torch.device("cpu")
93
+
94
+
95
+ class Predictor:
96
+ def __init__(self, args):
97
+ self.args = args
98
+ self.model_ = get_models(
99
+ device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft
100
+ )
101
+ self.model = ort.InferenceSession(
102
+ os.path.join(args.onnx, self.model_.target_name + ".onnx"),
103
+ providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
104
+ )
105
+ print("onnx load done")
106
+
107
+ def demix(self, mix):
108
+ samples = mix.shape[-1]
109
+ margin = self.args.margin
110
+ chunk_size = self.args.chunks * 44100
111
+ assert not margin == 0, "margin cannot be zero!"
112
+ if margin > chunk_size:
113
+ margin = chunk_size
114
+
115
+ segmented_mix = {}
116
+
117
+ if self.args.chunks == 0 or samples < chunk_size:
118
+ chunk_size = samples
119
+
120
+ counter = -1
121
+ for skip in range(0, samples, chunk_size):
122
+ counter += 1
123
+
124
+ s_margin = 0 if counter == 0 else margin
125
+ end = min(skip + chunk_size + margin, samples)
126
+
127
+ start = skip - s_margin
128
+
129
+ segmented_mix[skip] = mix[:, start:end].copy()
130
+ if end == samples:
131
+ break
132
+
133
+ sources = self.demix_base(segmented_mix, margin_size=margin)
134
+ """
135
+ mix:(2,big_sample)
136
+ segmented_mix:offset->(2,small_sample)
137
+ sources:(1,2,big_sample)
138
+ """
139
+ return sources
140
+
141
+ def demix_base(self, mixes, margin_size):
142
+ chunked_sources = []
143
+ progress_bar = tqdm(total=len(mixes))
144
+ progress_bar.set_description("Processing")
145
+ for mix in mixes:
146
+ cmix = mixes[mix]
147
+ sources = []
148
+ n_sample = cmix.shape[1]
149
+ model = self.model_
150
+ trim = model.n_fft // 2
151
+ gen_size = model.chunk_size - 2 * trim
152
+ pad = gen_size - n_sample % gen_size
153
+ mix_p = np.concatenate(
154
+ (np.zeros((2, trim)), cmix, np.zeros((2, pad)), np.zeros((2, trim))), 1
155
+ )
156
+ mix_waves = []
157
+ i = 0
158
+ while i < n_sample + pad:
159
+ waves = np.array(mix_p[:, i : i + model.chunk_size])
160
+ mix_waves.append(waves)
161
+ i += gen_size
162
+ mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu)
163
+ with torch.no_grad():
164
+ _ort = self.model
165
+ spek = model.stft(mix_waves)
166
+ if self.args.denoise:
167
+ spec_pred = (
168
+ -_ort.run(None, {"input": -spek.cpu().numpy()})[0] * 0.5
169
+ + _ort.run(None, {"input": spek.cpu().numpy()})[0] * 0.5
170
+ )
171
+ tar_waves = model.istft(torch.tensor(spec_pred))
172
+ else:
173
+ tar_waves = model.istft(
174
+ torch.tensor(_ort.run(None, {"input": spek.cpu().numpy()})[0])
175
+ )
176
+ tar_signal = (
177
+ tar_waves[:, :, trim:-trim]
178
+ .transpose(0, 1)
179
+ .reshape(2, -1)
180
+ .numpy()[:, :-pad]
181
+ )
182
+
183
+ start = 0 if mix == 0 else margin_size
184
+ end = None if mix == list(mixes.keys())[::-1][0] else -margin_size
185
+ if margin_size == 0:
186
+ end = None
187
+ sources.append(tar_signal[:, start:end])
188
+
189
+ progress_bar.update(1)
190
+
191
+ chunked_sources.append(sources)
192
+ _sources = np.concatenate(chunked_sources, axis=-1)
193
+ # del self.model
194
+ progress_bar.close()
195
+ return _sources
196
+
197
+ def prediction(self, m, vocal_root, others_root, format):
198
+ os.makedirs(vocal_root, exist_ok=True)
199
+ os.makedirs(others_root, exist_ok=True)
200
+ basename = os.path.basename(m)
201
+ mix, rate = librosa.load(m, mono=False, sr=44100)
202
+ if mix.ndim == 1:
203
+ mix = np.asfortranarray([mix, mix])
204
+ mix = mix.T
205
+ sources = self.demix(mix.T)
206
+ opt = sources[0].T
207
+ if format in ["wav", "flac"]:
208
+ sf.write(
209
+ "%s/%s_main_vocal.%s" % (vocal_root, basename, format), mix - opt, rate
210
+ )
211
+ sf.write("%s/%s_others.%s" % (others_root, basename, format), opt, rate)
212
+ else:
213
+ path_vocal = "%s/%s_main_vocal.wav" % (vocal_root, basename)
214
+ path_other = "%s/%s_others.wav" % (others_root, basename)
215
+ sf.write(path_vocal, mix - opt, rate)
216
+ sf.write(path_other, opt, rate)
217
+ if os.path.exists(path_vocal):
218
+ os.system(
219
+ "ffmpeg -i %s -vn %s -q:a 2 -y"
220
+ % (path_vocal, path_vocal[:-4] + ".%s" % format)
221
+ )
222
+ if os.path.exists(path_other):
223
+ os.system(
224
+ "ffmpeg -i %s -vn %s -q:a 2 -y"
225
+ % (path_other, path_other[:-4] + ".%s" % format)
226
+ )
227
+
228
+
229
+ class MDXNetDereverb:
230
+ def __init__(self, chunks):
231
+ self.onnx = "uvr5_weights/onnx_dereverb_By_FoxJoy"
232
+ self.shifts = 10 #'Predict with randomised equivariant stabilisation'
233
+ self.mixing = "min_mag" # ['default','min_mag','max_mag']
234
+ self.chunks = chunks
235
+ self.margin = 44100
236
+ self.dim_t = 9
237
+ self.dim_f = 3072
238
+ self.n_fft = 6144
239
+ self.denoise = True
240
+ self.pred = Predictor(self)
241
+
242
+ def _path_audio_(self, input, vocal_root, others_root, format):
243
+ self.pred.prediction(input, vocal_root, others_root, format)
244
+
245
+
246
+ if __name__ == "__main__":
247
+ dereverb = MDXNetDereverb(15)
248
+ from time import time as ttime
249
+
250
+ t0 = ttime()
251
+ dereverb._path_audio_(
252
+ "雪雪伴奏对消HP5.wav",
253
+ "vocal",
254
+ "others",
255
+ )
256
+ t1 = ttime()
257
+ print(t1 - t0)
258
+
259
+
260
+ """
261
+
262
+ runtime\python.exe MDXNet.py
263
+
264
+ 6G:
265
+ 15/9:0.8G->6.8G
266
+ 14:0.8G->6.5G
267
+ 25:炸
268
+
269
+ half15:0.7G->6.6G,22.69s
270
+ fp32-15:0.7G->6.6G,20.85s
271
+
272
+ """
Makefile ADDED
@@ -0,0 +1,63 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ .PHONY:
2
+ .ONESHELL:
3
+
4
+ help: ## Show this help and exit
5
+ @grep -hE '^[A-Za-z0-9_ \-]*?:.*##.*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}'
6
+
7
+ install: ## Install dependencies (Do everytime you start up a paperspace machine)
8
+ apt-get -y install build-essential python3-dev ffmpeg
9
+ pip install --upgrade setuptools wheel
10
+ pip install --upgrade pip
11
+ pip install faiss-gpu fairseq gradio ffmpeg ffmpeg-python praat-parselmouth pyworld numpy==1.23.5 numba==0.56.4 librosa==0.9.1
12
+ pip install -r requirements.txt
13
+ pip install --upgrade lxml
14
+ apt-get update
15
+ apt -y install -qq aria2
16
+
17
+ basev1: ## Download version 1 pre-trained models (Do only once after cloning the fork)
18
+ mkdir -p pretrained uvr5_weights
19
+ git pull
20
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d pretrained -o D32k.pth
21
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d pretrained -o D40k.pth
22
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d pretrained -o D48k.pth
23
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d pretrained -o G32k.pth
24
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d pretrained -o G40k.pth
25
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G48k.pth -d pretrained -o G48k.pth
26
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth -d pretrained -o f0D32k.pth
27
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth -d pretrained -o f0D40k.pth
28
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth -d pretrained -o f0D48k.pth
29
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth -d pretrained -o f0G32k.pth
30
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth -d pretrained -o f0G40k.pth
31
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth -d pretrained -o f0G48k.pth
32
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d uvr5_weights -o HP2-人声vocals+非人声instrumentals.pth
33
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d uvr5_weights -o HP5-主旋律人声vocals+其他instrumentals.pth
34
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d ./ -o hubert_base.pt
35
+
36
+ basev2: ## Download version 2 pre-trained models (Do only once after cloning the fork)
37
+ mkdir -p pretrained_v2 uvr5_weights
38
+ git pull
39
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D32k.pth -d pretrained_v2 -o D32k.pth
40
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d pretrained_v2 -o D40k.pth
41
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D48k.pth -d pretrained_v2 -o D48k.pth
42
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G32k.pth -d pretrained_v2 -o G32k.pth
43
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d pretrained_v2 -o G40k.pth
44
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G48k.pth -d pretrained_v2 -o G48k.pth
45
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D32k.pth -d pretrained_v2 -o f0D32k.pth
46
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth -d pretrained_v2 -o f0D40k.pth
47
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D48k.pth -d pretrained_v2 -o f0D48k.pth
48
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G32k.pth -d pretrained_v2 -o f0G32k.pth
49
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth -d pretrained_v2 -o f0G40k.pth
50
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G48k.pth -d pretrained_v2 -o f0G48k.pth
51
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d uvr5_weights -o HP2-人声vocals+非人声instrumentals.pth
52
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d uvr5_weights -o HP5-主旋律人声vocals+其他instrumentals.pth
53
+ aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d ./ -o hubert_base.pt
54
+
55
+ run-ui: ## Run the python GUI
56
+ python infer-web.py --paperspace --pycmd python
57
+
58
+ run-cli: ## Run the python CLI
59
+ python infer-web.py --pycmd python --is_cli
60
+
61
+ tensorboard: ## Start the tensorboard (Run on separate terminal)
62
+ echo https://tensorboard-$$(hostname).clg07azjl.paperspacegradient.com
63
+ tensorboard --logdir logs --bind_all
README.md CHANGED
@@ -1,3 +1,10 @@
1
  ---
 
 
 
 
2
  sdk: gradio
 
 
 
3
  ---
 
1
  ---
2
+ title: RVC Inference HF
3
+ emoji: 👀
4
+ colorFrom: green
5
+ colorTo: green
6
  sdk: gradio
7
+ sdk_version: 3.43.2
8
+ app_file: app.py
9
+ pinned: false
10
  ---
app.py ADDED
The diff for this file is too large to render. See raw diff
 
assets/hubert/.gitignore ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ *
2
+ !.gitignore
assets/pretrained/.gitignore ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ *
2
+ !.gitignore
assets/pretrained_v2/.gitignore ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ *
2
+ !.gitignore
assets/rmvpe/.gitignore ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ *
2
+ !.gitignore
assets/uvr5_weights/.gitignore ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ *
2
+ !.gitignore
assets/weights/.gitignore ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ *
2
+ !.gitignore
audioEffects.py ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from pedalboard import Pedalboard, Compressor, Reverb, NoiseGate
2
+ from pedalboard.io import AudioFile
3
+ import sys
4
+ import os
5
+ now_dir = os.getcwd()
6
+ sys.path.append(now_dir)
7
+ from i18n import I18nAuto
8
+ i18n = I18nAuto()
9
+ from pydub import AudioSegment
10
+ import numpy as np
11
+ import soundfile as sf
12
+ from pydub.playback import play
13
+
14
+ def process_audio(input_path, output_path, reverb_enabled, compressor_enabled, noise_gate_enabled, ):
15
+ print(reverb_enabled)
16
+ print(compressor_enabled)
17
+ print(noise_gate_enabled)
18
+ effects = []
19
+ if reverb_enabled:
20
+ effects.append(Reverb(room_size=0.01))
21
+ if compressor_enabled:
22
+ effects.append(Compressor(threshold_db=-10, ratio=25))
23
+ if noise_gate_enabled:
24
+ effects.append(NoiseGate(threshold_db=-16, ratio=1.5, release_ms=250))
25
+
26
+ board = Pedalboard(effects)
27
+
28
+ with AudioFile(input_path) as f:
29
+ with AudioFile(output_path, 'w', f.samplerate, f.num_channels) as o:
30
+ while f.tell() < f.frames:
31
+ chunk = f.read(f.samplerate)
32
+ effected = board(chunk, f.samplerate, reset=False)
33
+ o.write(effected)
34
+
35
+ result = i18n("Processed audio saved at: ") + output_path
36
+ print(result)
37
+ return output_path
audios/.gitignore ADDED
File without changes
colab_for_mdx.py ADDED
@@ -0,0 +1,71 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json
2
+ import os
3
+ import gc
4
+ import psutil
5
+ import requests
6
+ import subprocess
7
+ import time
8
+ import logging
9
+ import sys
10
+ import shutil
11
+ now_dir = os.getcwd()
12
+ sys.path.append(now_dir)
13
+ first_cell_executed = False
14
+ file_folder = "Colab-for-MDX_B"
15
+ def first_cell_ran():
16
+ global first_cell_executed
17
+ if first_cell_executed:
18
+ #print("The 'first_cell_ran' function has already been executed.")
19
+ return
20
+
21
+
22
+
23
+ first_cell_executed = True
24
+ os.makedirs("tmp_models", exist_ok=True)
25
+
26
+
27
+
28
+ class hide_opt: # hide outputs
29
+ def __enter__(self):
30
+ self._original_stdout = sys.stdout
31
+ sys.stdout = open(os.devnull, "w")
32
+
33
+ def __exit__(self, exc_type, exc_val, exc_tb):
34
+ sys.stdout.close()
35
+ sys.stdout = self._original_stdout
36
+
37
+ def get_size(bytes, suffix="B"): # read ram
38
+ global svmem
39
+ factor = 1024
40
+ for unit in ["", "K", "M", "G", "T", "P"]:
41
+ if bytes < factor:
42
+ return f"{bytes:.2f}{unit}{suffix}"
43
+ bytes /= factor
44
+ svmem = psutil.virtual_memory()
45
+
46
+
47
+ def use_uvr_without_saving():
48
+ print("Notice: files won't be saved to personal drive.")
49
+ print(f"Downloading {file_folder}...", end=" ")
50
+ with hide_opt():
51
+ #os.chdir(mounting_path)
52
+ items_to_move = ["demucs", "diffq","julius","model","separated","tracks","mdx.py","MDX-Net_Colab.ipynb"]
53
+ subprocess.run(["git", "clone", "https://github.com/NaJeongMo/Colab-for-MDX_B.git"])
54
+ for item_name in items_to_move:
55
+ item_path = os.path.join(file_folder, item_name)
56
+ if os.path.exists(item_path):
57
+ if os.path.isfile(item_path):
58
+ shutil.move(item_path, now_dir)
59
+ elif os.path.isdir(item_path):
60
+ shutil.move(item_path, now_dir)
61
+ try:
62
+ shutil.rmtree(file_folder)
63
+ except PermissionError:
64
+ print(f"No se pudo eliminar la carpeta {file_folder}. Puede estar relacionada con Git.")
65
+
66
+
67
+ use_uvr_without_saving()
68
+ print("done!")
69
+ if not os.path.exists("tracks"):
70
+ os.mkdir("tracks")
71
+ first_cell_ran()
configs/32k.json ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "train": {
3
+ "log_interval": 200,
4
+ "seed": 1234,
5
+ "epochs": 20000,
6
+ "learning_rate": 1e-4,
7
+ "betas": [0.8, 0.99],
8
+ "eps": 1e-9,
9
+ "batch_size": 4,
10
+ "fp16_run": false,
11
+ "lr_decay": 0.999875,
12
+ "segment_size": 12800,
13
+ "init_lr_ratio": 1,
14
+ "warmup_epochs": 0,
15
+ "c_mel": 45,
16
+ "c_kl": 1.0
17
+ },
18
+ "data": {
19
+ "max_wav_value": 32768.0,
20
+ "sampling_rate": 32000,
21
+ "filter_length": 1024,
22
+ "hop_length": 320,
23
+ "win_length": 1024,
24
+ "n_mel_channels": 80,
25
+ "mel_fmin": 0.0,
26
+ "mel_fmax": null
27
+ },
28
+ "model": {
29
+ "inter_channels": 192,
30
+ "hidden_channels": 192,
31
+ "filter_channels": 768,
32
+ "n_heads": 2,
33
+ "n_layers": 6,
34
+ "kernel_size": 3,
35
+ "p_dropout": 0,
36
+ "resblock": "1",
37
+ "resblock_kernel_sizes": [3, 7, 11],
38
+ "resblock_dilation_sizes": [
39
+ [1, 3, 5],
40
+ [1, 3, 5],
41
+ [1, 3, 5]
42
+ ],
43
+ "upsample_rates": [10, 4, 2, 2, 2],
44
+ "upsample_initial_channel": 512,
45
+ "upsample_kernel_sizes": [16, 16, 4, 4, 4],
46
+ "use_spectral_norm": false,
47
+ "gin_channels": 256,
48
+ "spk_embed_dim": 109
49
+ }
50
+ }
configs/32k_v2.json ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "train": {
3
+ "log_interval": 200,
4
+ "seed": 1234,
5
+ "epochs": 20000,
6
+ "learning_rate": 1e-4,
7
+ "betas": [0.8, 0.99],
8
+ "eps": 1e-9,
9
+ "batch_size": 4,
10
+ "fp16_run": true,
11
+ "lr_decay": 0.999875,
12
+ "segment_size": 12800,
13
+ "init_lr_ratio": 1,
14
+ "warmup_epochs": 0,
15
+ "c_mel": 45,
16
+ "c_kl": 1.0
17
+ },
18
+ "data": {
19
+ "max_wav_value": 32768.0,
20
+ "sampling_rate": 32000,
21
+ "filter_length": 1024,
22
+ "hop_length": 320,
23
+ "win_length": 1024,
24
+ "n_mel_channels": 80,
25
+ "mel_fmin": 0.0,
26
+ "mel_fmax": null
27
+ },
28
+ "model": {
29
+ "inter_channels": 192,
30
+ "hidden_channels": 192,
31
+ "filter_channels": 768,
32
+ "n_heads": 2,
33
+ "n_layers": 6,
34
+ "kernel_size": 3,
35
+ "p_dropout": 0,
36
+ "resblock": "1",
37
+ "resblock_kernel_sizes": [3, 7, 11],
38
+ "resblock_dilation_sizes": [
39
+ [1, 3, 5],
40
+ [1, 3, 5],
41
+ [1, 3, 5]
42
+ ],
43
+ "upsample_rates": [10, 8, 2, 2],
44
+ "upsample_initial_channel": 512,
45
+ "upsample_kernel_sizes": [20, 16, 4, 4],
46
+ "use_spectral_norm": false,
47
+ "gin_channels": 256,
48
+ "spk_embed_dim": 109
49
+ }
50
+ }
configs/40k.json ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "train": {
3
+ "log_interval": 200,
4
+ "seed": 1234,
5
+ "epochs": 20000,
6
+ "learning_rate": 1e-4,
7
+ "betas": [0.8, 0.99],
8
+ "eps": 1e-9,
9
+ "batch_size": 4,
10
+ "fp16_run": false,
11
+ "lr_decay": 0.999875,
12
+ "segment_size": 12800,
13
+ "init_lr_ratio": 1,
14
+ "warmup_epochs": 0,
15
+ "c_mel": 45,
16
+ "c_kl": 1.0
17
+ },
18
+ "data": {
19
+ "max_wav_value": 32768.0,
20
+ "sampling_rate": 40000,
21
+ "filter_length": 2048,
22
+ "hop_length": 400,
23
+ "win_length": 2048,
24
+ "n_mel_channels": 125,
25
+ "mel_fmin": 0.0,
26
+ "mel_fmax": null
27
+ },
28
+ "model": {
29
+ "inter_channels": 192,
30
+ "hidden_channels": 192,
31
+ "filter_channels": 768,
32
+ "n_heads": 2,
33
+ "n_layers": 6,
34
+ "kernel_size": 3,
35
+ "p_dropout": 0,
36
+ "resblock": "1",
37
+ "resblock_kernel_sizes": [3, 7, 11],
38
+ "resblock_dilation_sizes": [
39
+ [1, 3, 5],
40
+ [1, 3, 5],
41
+ [1, 3, 5]
42
+ ],
43
+ "upsample_rates": [10, 10, 2, 2],
44
+ "upsample_initial_channel": 512,
45
+ "upsample_kernel_sizes": [16, 16, 4, 4],
46
+ "use_spectral_norm": false,
47
+ "gin_channels": 256,
48
+ "spk_embed_dim": 109
49
+ }
50
+ }
configs/48k.json ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "train": {
3
+ "log_interval": 200,
4
+ "seed": 1234,
5
+ "epochs": 20000,
6
+ "learning_rate": 1e-4,
7
+ "betas": [0.8, 0.99],
8
+ "eps": 1e-9,
9
+ "batch_size": 4,
10
+ "fp16_run": false,
11
+ "lr_decay": 0.999875,
12
+ "segment_size": 11520,
13
+ "init_lr_ratio": 1,
14
+ "warmup_epochs": 0,
15
+ "c_mel": 45,
16
+ "c_kl": 1.0
17
+ },
18
+ "data": {
19
+ "max_wav_value": 32768.0,
20
+ "sampling_rate": 48000,
21
+ "filter_length": 2048,
22
+ "hop_length": 480,
23
+ "win_length": 2048,
24
+ "n_mel_channels": 128,
25
+ "mel_fmin": 0.0,
26
+ "mel_fmax": null
27
+ },
28
+ "model": {
29
+ "inter_channels": 192,
30
+ "hidden_channels": 192,
31
+ "filter_channels": 768,
32
+ "n_heads": 2,
33
+ "n_layers": 6,
34
+ "kernel_size": 3,
35
+ "p_dropout": 0,
36
+ "resblock": "1",
37
+ "resblock_kernel_sizes": [3, 7, 11],
38
+ "resblock_dilation_sizes": [
39
+ [1, 3, 5],
40
+ [1, 3, 5],
41
+ [1, 3, 5]
42
+ ],
43
+ "upsample_rates": [10, 6, 2, 2, 2],
44
+ "upsample_initial_channel": 512,
45
+ "upsample_kernel_sizes": [16, 16, 4, 4, 4],
46
+ "use_spectral_norm": false,
47
+ "gin_channels": 256,
48
+ "spk_embed_dim": 109
49
+ }
50
+ }
configs/48k_v2.json ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "train": {
3
+ "log_interval": 200,
4
+ "seed": 1234,
5
+ "epochs": 20000,
6
+ "learning_rate": 1e-4,
7
+ "betas": [0.8, 0.99],
8
+ "eps": 1e-9,
9
+ "batch_size": 4,
10
+ "fp16_run": true,
11
+ "lr_decay": 0.999875,
12
+ "segment_size": 17280,
13
+ "init_lr_ratio": 1,
14
+ "warmup_epochs": 0,
15
+ "c_mel": 45,
16
+ "c_kl": 1.0
17
+ },
18
+ "data": {
19
+ "max_wav_value": 32768.0,
20
+ "sampling_rate": 48000,
21
+ "filter_length": 2048,
22
+ "hop_length": 480,
23
+ "win_length": 2048,
24
+ "n_mel_channels": 128,
25
+ "mel_fmin": 0.0,
26
+ "mel_fmax": null
27
+ },
28
+ "model": {
29
+ "inter_channels": 192,
30
+ "hidden_channels": 192,
31
+ "filter_channels": 768,
32
+ "n_heads": 2,
33
+ "n_layers": 6,
34
+ "kernel_size": 3,
35
+ "p_dropout": 0,
36
+ "resblock": "1",
37
+ "resblock_kernel_sizes": [3, 7, 11],
38
+ "resblock_dilation_sizes": [
39
+ [1, 3, 5],
40
+ [1, 3, 5],
41
+ [1, 3, 5]
42
+ ],
43
+ "upsample_rates": [12, 10, 2, 2],
44
+ "upsample_initial_channel": 512,
45
+ "upsample_kernel_sizes": [24, 20, 4, 4],
46
+ "use_spectral_norm": false,
47
+ "gin_channels": 256,
48
+ "spk_embed_dim": 109
49
+ }
50
+ }
configs/config.json ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "pth_path": "assets/weights/kikiV1.pth",
3
+ "index_path": "logs/kikiV1.index",
4
+ "sg_input_device": "VoiceMeeter Output (VB-Audio Vo (MME)",
5
+ "sg_output_device": "VoiceMeeter Aux Input (VB-Audio (MME)",
6
+ "threhold": -45.0,
7
+ "pitch": 12.0,
8
+ "index_rate": 0.0,
9
+ "rms_mix_rate": 0.0,
10
+ "block_time": 0.25,
11
+ "crossfade_length": 0.04,
12
+ "extra_time": 2.0,
13
+ "n_cpu": 6.0,
14
+ "f0method": "rmvpe"
15
+ }
configs/config.py ADDED
@@ -0,0 +1,265 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+ import os
3
+ import sys
4
+ import json
5
+ from multiprocessing import cpu_count
6
+
7
+ import torch
8
+
9
+ try:
10
+ import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
11
+ if torch.xpu.is_available():
12
+ from infer.modules.ipex import ipex_init
13
+ ipex_init()
14
+ except Exception:
15
+ pass
16
+
17
+ import logging
18
+
19
+ logger = logging.getLogger(__name__)
20
+
21
+
22
+ version_config_list = [
23
+ "v1/32k.json",
24
+ "v1/40k.json",
25
+ "v1/48k.json",
26
+ "v2/48k.json",
27
+ "v2/32k.json",
28
+ ]
29
+
30
+
31
+ def singleton_variable(func):
32
+ def wrapper(*args, **kwargs):
33
+ if not wrapper.instance:
34
+ wrapper.instance = func(*args, **kwargs)
35
+ return wrapper.instance
36
+
37
+ wrapper.instance = None
38
+ return wrapper
39
+
40
+
41
+ @singleton_variable
42
+ class Config:
43
+ def __init__(self):
44
+ self.device = "cuda:0"
45
+ self.is_half = True
46
+ self.n_cpu = 0
47
+ self.gpu_name = None
48
+ self.json_config = self.load_config_json()
49
+ self.gpu_mem = None
50
+ (
51
+ self.python_cmd,
52
+ self.listen_port,
53
+ self.iscolab,
54
+ self.noparallel,
55
+ self.noautoopen,
56
+ self.paperspace,
57
+ self.is_cli,
58
+ self.grtheme,
59
+ self.dml,
60
+ ) = self.arg_parse()
61
+ self.instead = ""
62
+ self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
63
+
64
+ @staticmethod
65
+ def load_config_json() -> dict:
66
+ d = {}
67
+ for config_file in version_config_list:
68
+ with open(f"configs/{config_file}", "r") as f:
69
+ d[config_file] = json.load(f)
70
+ return d
71
+
72
+ @staticmethod
73
+ def arg_parse() -> tuple:
74
+ exe = sys.executable or "python"
75
+ parser = argparse.ArgumentParser()
76
+ parser.add_argument("--port", type=int, default=7865, help="Listen port")
77
+ parser.add_argument("--pycmd", type=str, default=exe, help="Python command")
78
+ parser.add_argument("--colab", action="store_true", help="Launch in colab")
79
+ parser.add_argument(
80
+ "--noparallel", action="store_true", help="Disable parallel processing"
81
+ )
82
+ parser.add_argument(
83
+ "--noautoopen",
84
+ action="store_true",
85
+ help="Do not open in browser automatically",
86
+ )
87
+ parser.add_argument(
88
+ "--paperspace",
89
+ action="store_true",
90
+ help="Note that this argument just shares a gradio link for the web UI. Thus can be used on other non-local CLI systems.",
91
+ )
92
+ parser.add_argument(
93
+ "--is_cli",
94
+ action="store_true",
95
+ help="Use the CLI instead of setting up a gradio UI. This flag will launch an RVC text interface where you can execute functions from infer-web.py!",
96
+ )
97
+
98
+ parser.add_argument(
99
+ "-t",
100
+ "--theme",
101
+ help = "Theme for Gradio. Format - `JohnSmith9982/small_and_pretty` (no backticks)",
102
+ default = "JohnSmith9982/small_and_pretty",
103
+ type = str
104
+ )
105
+
106
+ parser.add_argument(
107
+ "--dml",
108
+ action="store_true",
109
+ help="Use DirectML backend instead of CUDA."
110
+ )
111
+
112
+ cmd_opts = parser.parse_args()
113
+
114
+ cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
115
+
116
+ return (
117
+ cmd_opts.pycmd,
118
+ cmd_opts.port,
119
+ cmd_opts.colab,
120
+ cmd_opts.noparallel,
121
+ cmd_opts.noautoopen,
122
+ cmd_opts.paperspace,
123
+ cmd_opts.is_cli,
124
+ cmd_opts.theme,
125
+ cmd_opts.dml,
126
+ )
127
+
128
+ # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
129
+ # check `getattr` and try it for compatibility
130
+ @staticmethod
131
+ def has_mps() -> bool:
132
+ if not torch.backends.mps.is_available():
133
+ return False
134
+ try:
135
+ torch.zeros(1).to(torch.device("mps"))
136
+ return True
137
+ except Exception:
138
+ return False
139
+
140
+ @staticmethod
141
+ def has_xpu() -> bool:
142
+ if hasattr(torch, "xpu") and torch.xpu.is_available():
143
+ return True
144
+ else:
145
+ return False
146
+
147
+ def use_fp32_config(self):
148
+ for config_file in version_config_list:
149
+ self.json_config[config_file]["train"]["fp16_run"] = False
150
+
151
+ def device_config(self) -> tuple:
152
+ if torch.cuda.is_available():
153
+ if self.has_xpu():
154
+ self.device = self.instead = "xpu:0"
155
+ self.is_half = True
156
+ i_device = int(self.device.split(":")[-1])
157
+ self.gpu_name = torch.cuda.get_device_name(i_device)
158
+ if (
159
+ ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
160
+ or "P40" in self.gpu_name.upper()
161
+ or "P10" in self.gpu_name.upper()
162
+ or "1060" in self.gpu_name
163
+ or "1070" in self.gpu_name
164
+ or "1080" in self.gpu_name
165
+ ):
166
+ logger.info("Found GPU %s, force to fp32", self.gpu_name)
167
+ self.is_half = False
168
+ self.use_fp32_config()
169
+ else:
170
+ logger.info("Found GPU %s", self.gpu_name)
171
+ self.gpu_mem = int(
172
+ torch.cuda.get_device_properties(i_device).total_memory
173
+ / 1024
174
+ / 1024
175
+ / 1024
176
+ + 0.4
177
+ )
178
+ if self.gpu_mem <= 4:
179
+ with open("infer/modules/train/preprocess.py", "r") as f:
180
+ strr = f.read().replace("3.7", "3.0")
181
+ with open("infer/modules/train/preprocess.py", "w") as f:
182
+ f.write(strr)
183
+ elif self.has_mps():
184
+ logger.info("No supported Nvidia GPU found")
185
+ self.device = self.instead = "mps"
186
+ self.is_half = False
187
+ self.use_fp32_config()
188
+ else:
189
+ logger.info("No supported Nvidia GPU found")
190
+ self.device = self.instead = "cpu"
191
+ self.is_half = False
192
+ self.use_fp32_config()
193
+
194
+ if self.n_cpu == 0:
195
+ self.n_cpu = cpu_count()
196
+
197
+ if self.is_half:
198
+ # 6G显存配置
199
+ x_pad = 3
200
+ x_query = 10
201
+ x_center = 60
202
+ x_max = 65
203
+ else:
204
+ # 5G显存配置
205
+ x_pad = 1
206
+ x_query = 6
207
+ x_center = 38
208
+ x_max = 41
209
+
210
+ if self.gpu_mem is not None and self.gpu_mem <= 4:
211
+ x_pad = 1
212
+ x_query = 5
213
+ x_center = 30
214
+ x_max = 32
215
+ if self.dml:
216
+ logger.info("Use DirectML instead")
217
+ if (
218
+ os.path.exists(
219
+ "runtime\Lib\site-packages\onnxruntime\capi\DirectML.dll"
220
+ )
221
+ == False
222
+ ):
223
+ try:
224
+ os.rename(
225
+ "runtime\Lib\site-packages\onnxruntime",
226
+ "runtime\Lib\site-packages\onnxruntime-cuda",
227
+ )
228
+ except:
229
+ pass
230
+ try:
231
+ os.rename(
232
+ "runtime\Lib\site-packages\onnxruntime-dml",
233
+ "runtime\Lib\site-packages\onnxruntime",
234
+ )
235
+ except:
236
+ pass
237
+ # if self.device != "cpu":
238
+ import torch_directml
239
+
240
+ self.device = torch_directml.device(torch_directml.default_device())
241
+ self.is_half = False
242
+ else:
243
+ if self.instead:
244
+ logger.info(f"Use {self.instead} instead")
245
+ if (
246
+ os.path.exists(
247
+ "runtime\Lib\site-packages\onnxruntime\capi\onnxruntime_providers_cuda.dll"
248
+ )
249
+ == False
250
+ ):
251
+ try:
252
+ os.rename(
253
+ "runtime\Lib\site-packages\onnxruntime",
254
+ "runtime\Lib\site-packages\onnxruntime-dml",
255
+ )
256
+ except:
257
+ pass
258
+ try:
259
+ os.rename(
260
+ "runtime\Lib\site-packages\onnxruntime-cuda",
261
+ "runtime\Lib\site-packages\onnxruntime",
262
+ )
263
+ except:
264
+ pass
265
+ return x_pad, x_query, x_center, x_max
configs/v1/32k.json ADDED
@@ -0,0 +1,46 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "train": {
3
+ "log_interval": 200,
4
+ "seed": 1234,
5
+ "epochs": 20000,
6
+ "learning_rate": 1e-4,
7
+ "betas": [0.8, 0.99],
8
+ "eps": 1e-9,
9
+ "batch_size": 4,
10
+ "fp16_run": true,
11
+ "lr_decay": 0.999875,
12
+ "segment_size": 12800,
13
+ "init_lr_ratio": 1,
14
+ "warmup_epochs": 0,
15
+ "c_mel": 45,
16
+ "c_kl": 1.0
17
+ },
18
+ "data": {
19
+ "max_wav_value": 32768.0,
20
+ "sampling_rate": 32000,
21
+ "filter_length": 1024,
22
+ "hop_length": 320,
23
+ "win_length": 1024,
24
+ "n_mel_channels": 80,
25
+ "mel_fmin": 0.0,
26
+ "mel_fmax": null
27
+ },
28
+ "model": {
29
+ "inter_channels": 192,
30
+ "hidden_channels": 192,
31
+ "filter_channels": 768,
32
+ "n_heads": 2,
33
+ "n_layers": 6,
34
+ "kernel_size": 3,
35
+ "p_dropout": 0,
36
+ "resblock": "1",
37
+ "resblock_kernel_sizes": [3,7,11],
38
+ "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
39
+ "upsample_rates": [10,4,2,2,2],
40
+ "upsample_initial_channel": 512,
41
+ "upsample_kernel_sizes": [16,16,4,4,4],
42
+ "use_spectral_norm": false,
43
+ "gin_channels": 256,
44
+ "spk_embed_dim": 109
45
+ }
46
+ }
configs/v1/40k.json ADDED
@@ -0,0 +1,46 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "train": {
3
+ "log_interval": 200,
4
+ "seed": 1234,
5
+ "epochs": 20000,
6
+ "learning_rate": 1e-4,
7
+ "betas": [0.8, 0.99],
8
+ "eps": 1e-9,
9
+ "batch_size": 4,
10
+ "fp16_run": true,
11
+ "lr_decay": 0.999875,
12
+ "segment_size": 12800,
13
+ "init_lr_ratio": 1,
14
+ "warmup_epochs": 0,
15
+ "c_mel": 45,
16
+ "c_kl": 1.0
17
+ },
18
+ "data": {
19
+ "max_wav_value": 32768.0,
20
+ "sampling_rate": 40000,
21
+ "filter_length": 2048,
22
+ "hop_length": 400,
23
+ "win_length": 2048,
24
+ "n_mel_channels": 125,
25
+ "mel_fmin": 0.0,
26
+ "mel_fmax": null
27
+ },
28
+ "model": {
29
+ "inter_channels": 192,
30
+ "hidden_channels": 192,
31
+ "filter_channels": 768,
32
+ "n_heads": 2,
33
+ "n_layers": 6,
34
+ "kernel_size": 3,
35
+ "p_dropout": 0,
36
+ "resblock": "1",
37
+ "resblock_kernel_sizes": [3,7,11],
38
+ "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
39
+ "upsample_rates": [10,10,2,2],
40
+ "upsample_initial_channel": 512,
41
+ "upsample_kernel_sizes": [16,16,4,4],
42
+ "use_spectral_norm": false,
43
+ "gin_channels": 256,
44
+ "spk_embed_dim": 109
45
+ }
46
+ }
configs/v1/48k.json ADDED
@@ -0,0 +1,46 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "train": {
3
+ "log_interval": 200,
4
+ "seed": 1234,
5
+ "epochs": 20000,
6
+ "learning_rate": 1e-4,
7
+ "betas": [0.8, 0.99],
8
+ "eps": 1e-9,
9
+ "batch_size": 4,
10
+ "fp16_run": true,
11
+ "lr_decay": 0.999875,
12
+ "segment_size": 11520,
13
+ "init_lr_ratio": 1,
14
+ "warmup_epochs": 0,
15
+ "c_mel": 45,
16
+ "c_kl": 1.0
17
+ },
18
+ "data": {
19
+ "max_wav_value": 32768.0,
20
+ "sampling_rate": 48000,
21
+ "filter_length": 2048,
22
+ "hop_length": 480,
23
+ "win_length": 2048,
24
+ "n_mel_channels": 128,
25
+ "mel_fmin": 0.0,
26
+ "mel_fmax": null
27
+ },
28
+ "model": {
29
+ "inter_channels": 192,
30
+ "hidden_channels": 192,
31
+ "filter_channels": 768,
32
+ "n_heads": 2,
33
+ "n_layers": 6,
34
+ "kernel_size": 3,
35
+ "p_dropout": 0,
36
+ "resblock": "1",
37
+ "resblock_kernel_sizes": [3,7,11],
38
+ "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
39
+ "upsample_rates": [10,6,2,2,2],
40
+ "upsample_initial_channel": 512,
41
+ "upsample_kernel_sizes": [16,16,4,4,4],
42
+ "use_spectral_norm": false,
43
+ "gin_channels": 256,
44
+ "spk_embed_dim": 109
45
+ }
46
+ }
configs/v2/32k.json ADDED
@@ -0,0 +1,46 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "train": {
3
+ "log_interval": 200,
4
+ "seed": 1234,
5
+ "epochs": 20000,
6
+ "learning_rate": 1e-4,
7
+ "betas": [0.8, 0.99],
8
+ "eps": 1e-9,
9
+ "batch_size": 4,
10
+ "fp16_run": true,
11
+ "lr_decay": 0.999875,
12
+ "segment_size": 12800,
13
+ "init_lr_ratio": 1,
14
+ "warmup_epochs": 0,
15
+ "c_mel": 45,
16
+ "c_kl": 1.0
17
+ },
18
+ "data": {
19
+ "max_wav_value": 32768.0,
20
+ "sampling_rate": 32000,
21
+ "filter_length": 1024,
22
+ "hop_length": 320,
23
+ "win_length": 1024,
24
+ "n_mel_channels": 80,
25
+ "mel_fmin": 0.0,
26
+ "mel_fmax": null
27
+ },
28
+ "model": {
29
+ "inter_channels": 192,
30
+ "hidden_channels": 192,
31
+ "filter_channels": 768,
32
+ "n_heads": 2,
33
+ "n_layers": 6,
34
+ "kernel_size": 3,
35
+ "p_dropout": 0,
36
+ "resblock": "1",
37
+ "resblock_kernel_sizes": [3,7,11],
38
+ "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
39
+ "upsample_rates": [10,8,2,2],
40
+ "upsample_initial_channel": 512,
41
+ "upsample_kernel_sizes": [20,16,4,4],
42
+ "use_spectral_norm": false,
43
+ "gin_channels": 256,
44
+ "spk_embed_dim": 109
45
+ }
46
+ }
configs/v2/48k.json ADDED
@@ -0,0 +1,46 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "train": {
3
+ "log_interval": 200,
4
+ "seed": 1234,
5
+ "epochs": 20000,
6
+ "learning_rate": 1e-4,
7
+ "betas": [0.8, 0.99],
8
+ "eps": 1e-9,
9
+ "batch_size": 4,
10
+ "fp16_run": true,
11
+ "lr_decay": 0.999875,
12
+ "segment_size": 17280,
13
+ "init_lr_ratio": 1,
14
+ "warmup_epochs": 0,
15
+ "c_mel": 45,
16
+ "c_kl": 1.0
17
+ },
18
+ "data": {
19
+ "max_wav_value": 32768.0,
20
+ "sampling_rate": 48000,
21
+ "filter_length": 2048,
22
+ "hop_length": 480,
23
+ "win_length": 2048,
24
+ "n_mel_channels": 128,
25
+ "mel_fmin": 0.0,
26
+ "mel_fmax": null
27
+ },
28
+ "model": {
29
+ "inter_channels": 192,
30
+ "hidden_channels": 192,
31
+ "filter_channels": 768,
32
+ "n_heads": 2,
33
+ "n_layers": 6,
34
+ "kernel_size": 3,
35
+ "p_dropout": 0,
36
+ "resblock": "1",
37
+ "resblock_kernel_sizes": [3,7,11],
38
+ "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
39
+ "upsample_rates": [12,10,2,2],
40
+ "upsample_initial_channel": 512,
41
+ "upsample_kernel_sizes": [24,20,4,4],
42
+ "use_spectral_norm": false,
43
+ "gin_channels": 256,
44
+ "spk_embed_dim": 109
45
+ }
46
+ }
csvdb/formanting.csv ADDED
File without changes
csvdb/stop.csv ADDED
File without changes
demucs/__init__.py ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Facebook, Inc. and its affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ __version__ = "2.0.3"
demucs/__main__.py ADDED
@@ -0,0 +1,317 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Facebook, Inc. and its affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import json
8
+ import math
9
+ import os
10
+ import sys
11
+ import time
12
+ from dataclasses import dataclass, field
13
+
14
+ import torch as th
15
+ from torch import distributed, nn
16
+ from torch.nn.parallel.distributed import DistributedDataParallel
17
+
18
+ from .augment import FlipChannels, FlipSign, Remix, Scale, Shift
19
+ from .compressed import get_compressed_datasets
20
+ from .model import Demucs
21
+ from .parser import get_name, get_parser
22
+ from .raw import Rawset
23
+ from .repitch import RepitchedWrapper
24
+ from .pretrained import load_pretrained, SOURCES
25
+ from .tasnet import ConvTasNet
26
+ from .test import evaluate
27
+ from .train import train_model, validate_model
28
+ from .utils import (human_seconds, load_model, save_model, get_state,
29
+ save_state, sizeof_fmt, get_quantizer)
30
+ from .wav import get_wav_datasets, get_musdb_wav_datasets
31
+
32
+
33
+ @dataclass
34
+ class SavedState:
35
+ metrics: list = field(default_factory=list)
36
+ last_state: dict = None
37
+ best_state: dict = None
38
+ optimizer: dict = None
39
+
40
+
41
+ def main():
42
+ parser = get_parser()
43
+ args = parser.parse_args()
44
+ name = get_name(parser, args)
45
+ print(f"Experiment {name}")
46
+
47
+ if args.musdb is None and args.rank == 0:
48
+ print(
49
+ "You must provide the path to the MusDB dataset with the --musdb flag. "
50
+ "To download the MusDB dataset, see https://sigsep.github.io/datasets/musdb.html.",
51
+ file=sys.stderr)
52
+ sys.exit(1)
53
+
54
+ eval_folder = args.evals / name
55
+ eval_folder.mkdir(exist_ok=True, parents=True)
56
+ args.logs.mkdir(exist_ok=True)
57
+ metrics_path = args.logs / f"{name}.json"
58
+ eval_folder.mkdir(exist_ok=True, parents=True)
59
+ args.checkpoints.mkdir(exist_ok=True, parents=True)
60
+ args.models.mkdir(exist_ok=True, parents=True)
61
+
62
+ if args.device is None:
63
+ device = "cpu"
64
+ if th.cuda.is_available():
65
+ device = "cuda"
66
+ else:
67
+ device = args.device
68
+
69
+ th.manual_seed(args.seed)
70
+ # Prevents too many threads to be started when running `museval` as it can be quite
71
+ # inefficient on NUMA architectures.
72
+ os.environ["OMP_NUM_THREADS"] = "1"
73
+ os.environ["MKL_NUM_THREADS"] = "1"
74
+
75
+ if args.world_size > 1:
76
+ if device != "cuda" and args.rank == 0:
77
+ print("Error: distributed training is only available with cuda device", file=sys.stderr)
78
+ sys.exit(1)
79
+ th.cuda.set_device(args.rank % th.cuda.device_count())
80
+ distributed.init_process_group(backend="nccl",
81
+ init_method="tcp://" + args.master,
82
+ rank=args.rank,
83
+ world_size=args.world_size)
84
+
85
+ checkpoint = args.checkpoints / f"{name}.th"
86
+ checkpoint_tmp = args.checkpoints / f"{name}.th.tmp"
87
+ if args.restart and checkpoint.exists() and args.rank == 0:
88
+ checkpoint.unlink()
89
+
90
+ if args.test or args.test_pretrained:
91
+ args.epochs = 1
92
+ args.repeat = 0
93
+ if args.test:
94
+ model = load_model(args.models / args.test)
95
+ else:
96
+ model = load_pretrained(args.test_pretrained)
97
+ elif args.tasnet:
98
+ model = ConvTasNet(audio_channels=args.audio_channels,
99
+ samplerate=args.samplerate, X=args.X,
100
+ segment_length=4 * args.samples,
101
+ sources=SOURCES)
102
+ else:
103
+ model = Demucs(
104
+ audio_channels=args.audio_channels,
105
+ channels=args.channels,
106
+ context=args.context,
107
+ depth=args.depth,
108
+ glu=args.glu,
109
+ growth=args.growth,
110
+ kernel_size=args.kernel_size,
111
+ lstm_layers=args.lstm_layers,
112
+ rescale=args.rescale,
113
+ rewrite=args.rewrite,
114
+ stride=args.conv_stride,
115
+ resample=args.resample,
116
+ normalize=args.normalize,
117
+ samplerate=args.samplerate,
118
+ segment_length=4 * args.samples,
119
+ sources=SOURCES,
120
+ )
121
+ model.to(device)
122
+ if args.init:
123
+ model.load_state_dict(load_pretrained(args.init).state_dict())
124
+
125
+ if args.show:
126
+ print(model)
127
+ size = sizeof_fmt(4 * sum(p.numel() for p in model.parameters()))
128
+ print(f"Model size {size}")
129
+ return
130
+
131
+ try:
132
+ saved = th.load(checkpoint, map_location='cpu')
133
+ except IOError:
134
+ saved = SavedState()
135
+
136
+ optimizer = th.optim.Adam(model.parameters(), lr=args.lr)
137
+
138
+ quantizer = None
139
+ quantizer = get_quantizer(model, args, optimizer)
140
+
141
+ if saved.last_state is not None:
142
+ model.load_state_dict(saved.last_state, strict=False)
143
+ if saved.optimizer is not None:
144
+ optimizer.load_state_dict(saved.optimizer)
145
+
146
+ model_name = f"{name}.th"
147
+ if args.save_model:
148
+ if args.rank == 0:
149
+ model.to("cpu")
150
+ model.load_state_dict(saved.best_state)
151
+ save_model(model, quantizer, args, args.models / model_name)
152
+ return
153
+ elif args.save_state:
154
+ model_name = f"{args.save_state}.th"
155
+ if args.rank == 0:
156
+ model.to("cpu")
157
+ model.load_state_dict(saved.best_state)
158
+ state = get_state(model, quantizer)
159
+ save_state(state, args.models / model_name)
160
+ return
161
+
162
+ if args.rank == 0:
163
+ done = args.logs / f"{name}.done"
164
+ if done.exists():
165
+ done.unlink()
166
+
167
+ augment = [Shift(args.data_stride)]
168
+ if args.augment:
169
+ augment += [FlipSign(), FlipChannels(), Scale(),
170
+ Remix(group_size=args.remix_group_size)]
171
+ augment = nn.Sequential(*augment).to(device)
172
+ print("Agumentation pipeline:", augment)
173
+
174
+ if args.mse:
175
+ criterion = nn.MSELoss()
176
+ else:
177
+ criterion = nn.L1Loss()
178
+
179
+ # Setting number of samples so that all convolution windows are full.
180
+ # Prevents hard to debug mistake with the prediction being shifted compared
181
+ # to the input mixture.
182
+ samples = model.valid_length(args.samples)
183
+ print(f"Number of training samples adjusted to {samples}")
184
+ samples = samples + args.data_stride
185
+ if args.repitch:
186
+ # We need a bit more audio samples, to account for potential
187
+ # tempo change.
188
+ samples = math.ceil(samples / (1 - 0.01 * args.max_tempo))
189
+
190
+ args.metadata.mkdir(exist_ok=True, parents=True)
191
+ if args.raw:
192
+ train_set = Rawset(args.raw / "train",
193
+ samples=samples,
194
+ channels=args.audio_channels,
195
+ streams=range(1, len(model.sources) + 1),
196
+ stride=args.data_stride)
197
+
198
+ valid_set = Rawset(args.raw / "valid", channels=args.audio_channels)
199
+ elif args.wav:
200
+ train_set, valid_set = get_wav_datasets(args, samples, model.sources)
201
+ elif args.is_wav:
202
+ train_set, valid_set = get_musdb_wav_datasets(args, samples, model.sources)
203
+ else:
204
+ train_set, valid_set = get_compressed_datasets(args, samples)
205
+
206
+ if args.repitch:
207
+ train_set = RepitchedWrapper(
208
+ train_set,
209
+ proba=args.repitch,
210
+ max_tempo=args.max_tempo)
211
+
212
+ best_loss = float("inf")
213
+ for epoch, metrics in enumerate(saved.metrics):
214
+ print(f"Epoch {epoch:03d}: "
215
+ f"train={metrics['train']:.8f} "
216
+ f"valid={metrics['valid']:.8f} "
217
+ f"best={metrics['best']:.4f} "
218
+ f"ms={metrics.get('true_model_size', 0):.2f}MB "
219
+ f"cms={metrics.get('compressed_model_size', 0):.2f}MB "
220
+ f"duration={human_seconds(metrics['duration'])}")
221
+ best_loss = metrics['best']
222
+
223
+ if args.world_size > 1:
224
+ dmodel = DistributedDataParallel(model,
225
+ device_ids=[th.cuda.current_device()],
226
+ output_device=th.cuda.current_device())
227
+ else:
228
+ dmodel = model
229
+
230
+ for epoch in range(len(saved.metrics), args.epochs):
231
+ begin = time.time()
232
+ model.train()
233
+ train_loss, model_size = train_model(
234
+ epoch, train_set, dmodel, criterion, optimizer, augment,
235
+ quantizer=quantizer,
236
+ batch_size=args.batch_size,
237
+ device=device,
238
+ repeat=args.repeat,
239
+ seed=args.seed,
240
+ diffq=args.diffq,
241
+ workers=args.workers,
242
+ world_size=args.world_size)
243
+ model.eval()
244
+ valid_loss = validate_model(
245
+ epoch, valid_set, model, criterion,
246
+ device=device,
247
+ rank=args.rank,
248
+ split=args.split_valid,
249
+ overlap=args.overlap,
250
+ world_size=args.world_size)
251
+
252
+ ms = 0
253
+ cms = 0
254
+ if quantizer and args.rank == 0:
255
+ ms = quantizer.true_model_size()
256
+ cms = quantizer.compressed_model_size(num_workers=min(40, args.world_size * 10))
257
+
258
+ duration = time.time() - begin
259
+ if valid_loss < best_loss and ms <= args.ms_target:
260
+ best_loss = valid_loss
261
+ saved.best_state = {
262
+ key: value.to("cpu").clone()
263
+ for key, value in model.state_dict().items()
264
+ }
265
+
266
+ saved.metrics.append({
267
+ "train": train_loss,
268
+ "valid": valid_loss,
269
+ "best": best_loss,
270
+ "duration": duration,
271
+ "model_size": model_size,
272
+ "true_model_size": ms,
273
+ "compressed_model_size": cms,
274
+ })
275
+ if args.rank == 0:
276
+ json.dump(saved.metrics, open(metrics_path, "w"))
277
+
278
+ saved.last_state = model.state_dict()
279
+ saved.optimizer = optimizer.state_dict()
280
+ if args.rank == 0 and not args.test:
281
+ th.save(saved, checkpoint_tmp)
282
+ checkpoint_tmp.rename(checkpoint)
283
+
284
+ print(f"Epoch {epoch:03d}: "
285
+ f"train={train_loss:.8f} valid={valid_loss:.8f} best={best_loss:.4f} ms={ms:.2f}MB "
286
+ f"cms={cms:.2f}MB "
287
+ f"duration={human_seconds(duration)}")
288
+
289
+ if args.world_size > 1:
290
+ distributed.barrier()
291
+
292
+ del dmodel
293
+ model.load_state_dict(saved.best_state)
294
+ if args.eval_cpu:
295
+ device = "cpu"
296
+ model.to(device)
297
+ model.eval()
298
+ evaluate(model, args.musdb, eval_folder,
299
+ is_wav=args.is_wav,
300
+ rank=args.rank,
301
+ world_size=args.world_size,
302
+ device=device,
303
+ save=args.save,
304
+ split=args.split_valid,
305
+ shifts=args.shifts,
306
+ overlap=args.overlap,
307
+ workers=args.eval_workers)
308
+ model.to("cpu")
309
+ if args.rank == 0:
310
+ if not (args.test or args.test_pretrained):
311
+ save_model(model, quantizer, args, args.models / model_name)
312
+ print("done")
313
+ done.write_text("done")
314
+
315
+
316
+ if __name__ == "__main__":
317
+ main()
demucs/audio.py ADDED
@@ -0,0 +1,172 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Facebook, Inc. and its affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+ import json
7
+ import subprocess as sp
8
+ from pathlib import Path
9
+
10
+ import julius
11
+ import numpy as np
12
+ import torch
13
+
14
+ from .utils import temp_filenames
15
+
16
+
17
+ def _read_info(path):
18
+ stdout_data = sp.check_output([
19
+ 'ffprobe', "-loglevel", "panic",
20
+ str(path), '-print_format', 'json', '-show_format', '-show_streams'
21
+ ])
22
+ return json.loads(stdout_data.decode('utf-8'))
23
+
24
+
25
+ class AudioFile:
26
+ """
27
+ Allows to read audio from any format supported by ffmpeg, as well as resampling or
28
+ converting to mono on the fly. See :method:`read` for more details.
29
+ """
30
+ def __init__(self, path: Path):
31
+ self.path = Path(path)
32
+ self._info = None
33
+
34
+ def __repr__(self):
35
+ features = [("path", self.path)]
36
+ features.append(("samplerate", self.samplerate()))
37
+ features.append(("channels", self.channels()))
38
+ features.append(("streams", len(self)))
39
+ features_str = ", ".join(f"{name}={value}" for name, value in features)
40
+ return f"AudioFile({features_str})"
41
+
42
+ @property
43
+ def info(self):
44
+ if self._info is None:
45
+ self._info = _read_info(self.path)
46
+ return self._info
47
+
48
+ @property
49
+ def duration(self):
50
+ return float(self.info['format']['duration'])
51
+
52
+ @property
53
+ def _audio_streams(self):
54
+ return [
55
+ index for index, stream in enumerate(self.info["streams"])
56
+ if stream["codec_type"] == "audio"
57
+ ]
58
+
59
+ def __len__(self):
60
+ return len(self._audio_streams)
61
+
62
+ def channels(self, stream=0):
63
+ return int(self.info['streams'][self._audio_streams[stream]]['channels'])
64
+
65
+ def samplerate(self, stream=0):
66
+ return int(self.info['streams'][self._audio_streams[stream]]['sample_rate'])
67
+
68
+ def read(self,
69
+ seek_time=None,
70
+ duration=None,
71
+ streams=slice(None),
72
+ samplerate=None,
73
+ channels=None,
74
+ temp_folder=None):
75
+ """
76
+ Slightly more efficient implementation than stempeg,
77
+ in particular, this will extract all stems at once
78
+ rather than having to loop over one file multiple times
79
+ for each stream.
80
+
81
+ Args:
82
+ seek_time (float): seek time in seconds or None if no seeking is needed.
83
+ duration (float): duration in seconds to extract or None to extract until the end.
84
+ streams (slice, int or list): streams to extract, can be a single int, a list or
85
+ a slice. If it is a slice or list, the output will be of size [S, C, T]
86
+ with S the number of streams, C the number of channels and T the number of samples.
87
+ If it is an int, the output will be [C, T].
88
+ samplerate (int): if provided, will resample on the fly. If None, no resampling will
89
+ be done. Original sampling rate can be obtained with :method:`samplerate`.
90
+ channels (int): if 1, will convert to mono. We do not rely on ffmpeg for that
91
+ as ffmpeg automatically scale by +3dB to conserve volume when playing on speakers.
92
+ See https://sound.stackexchange.com/a/42710.
93
+ Our definition of mono is simply the average of the two channels. Any other
94
+ value will be ignored.
95
+ temp_folder (str or Path or None): temporary folder to use for decoding.
96
+
97
+
98
+ """
99
+ streams = np.array(range(len(self)))[streams]
100
+ single = not isinstance(streams, np.ndarray)
101
+ if single:
102
+ streams = [streams]
103
+
104
+ if duration is None:
105
+ target_size = None
106
+ query_duration = None
107
+ else:
108
+ target_size = int((samplerate or self.samplerate()) * duration)
109
+ query_duration = float((target_size + 1) / (samplerate or self.samplerate()))
110
+
111
+ with temp_filenames(len(streams)) as filenames:
112
+ command = ['ffmpeg', '-y']
113
+ command += ['-loglevel', 'panic']
114
+ if seek_time:
115
+ command += ['-ss', str(seek_time)]
116
+ command += ['-i', str(self.path)]
117
+ for stream, filename in zip(streams, filenames):
118
+ command += ['-map', f'0:{self._audio_streams[stream]}']
119
+ if query_duration is not None:
120
+ command += ['-t', str(query_duration)]
121
+ command += ['-threads', '1']
122
+ command += ['-f', 'f32le']
123
+ if samplerate is not None:
124
+ command += ['-ar', str(samplerate)]
125
+ command += [filename]
126
+
127
+ sp.run(command, check=True)
128
+ wavs = []
129
+ for filename in filenames:
130
+ wav = np.fromfile(filename, dtype=np.float32)
131
+ wav = torch.from_numpy(wav)
132
+ wav = wav.view(-1, self.channels()).t()
133
+ if channels is not None:
134
+ wav = convert_audio_channels(wav, channels)
135
+ if target_size is not None:
136
+ wav = wav[..., :target_size]
137
+ wavs.append(wav)
138
+ wav = torch.stack(wavs, dim=0)
139
+ if single:
140
+ wav = wav[0]
141
+ return wav
142
+
143
+
144
+ def convert_audio_channels(wav, channels=2):
145
+ """Convert audio to the given number of channels."""
146
+ *shape, src_channels, length = wav.shape
147
+ if src_channels == channels:
148
+ pass
149
+ elif channels == 1:
150
+ # Case 1:
151
+ # The caller asked 1-channel audio, but the stream have multiple
152
+ # channels, downmix all channels.
153
+ wav = wav.mean(dim=-2, keepdim=True)
154
+ elif src_channels == 1:
155
+ # Case 2:
156
+ # The caller asked for multiple channels, but the input file have
157
+ # one single channel, replicate the audio over all channels.
158
+ wav = wav.expand(*shape, channels, length)
159
+ elif src_channels >= channels:
160
+ # Case 3:
161
+ # The caller asked for multiple channels, and the input file have
162
+ # more channels than requested. In that case return the first channels.
163
+ wav = wav[..., :channels, :]
164
+ else:
165
+ # Case 4: What is a reasonable choice here?
166
+ raise ValueError('The audio file has less channels than requested but is not mono.')
167
+ return wav
168
+
169
+
170
+ def convert_audio(wav, from_samplerate, to_samplerate, channels):
171
+ wav = convert_audio_channels(wav, channels)
172
+ return julius.resample_frac(wav, from_samplerate, to_samplerate)
demucs/augment.py ADDED
@@ -0,0 +1,106 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Facebook, Inc. and its affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import random
8
+ import torch as th
9
+ from torch import nn
10
+
11
+
12
+ class Shift(nn.Module):
13
+ """
14
+ Randomly shift audio in time by up to `shift` samples.
15
+ """
16
+ def __init__(self, shift=8192):
17
+ super().__init__()
18
+ self.shift = shift
19
+
20
+ def forward(self, wav):
21
+ batch, sources, channels, time = wav.size()
22
+ length = time - self.shift
23
+ if self.shift > 0:
24
+ if not self.training:
25
+ wav = wav[..., :length]
26
+ else:
27
+ offsets = th.randint(self.shift, [batch, sources, 1, 1], device=wav.device)
28
+ offsets = offsets.expand(-1, -1, channels, -1)
29
+ indexes = th.arange(length, device=wav.device)
30
+ wav = wav.gather(3, indexes + offsets)
31
+ return wav
32
+
33
+
34
+ class FlipChannels(nn.Module):
35
+ """
36
+ Flip left-right channels.
37
+ """
38
+ def forward(self, wav):
39
+ batch, sources, channels, time = wav.size()
40
+ if self.training and wav.size(2) == 2:
41
+ left = th.randint(2, (batch, sources, 1, 1), device=wav.device)
42
+ left = left.expand(-1, -1, -1, time)
43
+ right = 1 - left
44
+ wav = th.cat([wav.gather(2, left), wav.gather(2, right)], dim=2)
45
+ return wav
46
+
47
+
48
+ class FlipSign(nn.Module):
49
+ """
50
+ Random sign flip.
51
+ """
52
+ def forward(self, wav):
53
+ batch, sources, channels, time = wav.size()
54
+ if self.training:
55
+ signs = th.randint(2, (batch, sources, 1, 1), device=wav.device, dtype=th.float32)
56
+ wav = wav * (2 * signs - 1)
57
+ return wav
58
+
59
+
60
+ class Remix(nn.Module):
61
+ """
62
+ Shuffle sources to make new mixes.
63
+ """
64
+ def __init__(self, group_size=4):
65
+ """
66
+ Shuffle sources within one batch.
67
+ Each batch is divided into groups of size `group_size` and shuffling is done within
68
+ each group separatly. This allow to keep the same probability distribution no matter
69
+ the number of GPUs. Without this grouping, using more GPUs would lead to a higher
70
+ probability of keeping two sources from the same track together which can impact
71
+ performance.
72
+ """
73
+ super().__init__()
74
+ self.group_size = group_size
75
+
76
+ def forward(self, wav):
77
+ batch, streams, channels, time = wav.size()
78
+ device = wav.device
79
+
80
+ if self.training:
81
+ group_size = self.group_size or batch
82
+ if batch % group_size != 0:
83
+ raise ValueError(f"Batch size {batch} must be divisible by group size {group_size}")
84
+ groups = batch // group_size
85
+ wav = wav.view(groups, group_size, streams, channels, time)
86
+ permutations = th.argsort(th.rand(groups, group_size, streams, 1, 1, device=device),
87
+ dim=1)
88
+ wav = wav.gather(1, permutations.expand(-1, -1, -1, channels, time))
89
+ wav = wav.view(batch, streams, channels, time)
90
+ return wav
91
+
92
+
93
+ class Scale(nn.Module):
94
+ def __init__(self, proba=1., min=0.25, max=1.25):
95
+ super().__init__()
96
+ self.proba = proba
97
+ self.min = min
98
+ self.max = max
99
+
100
+ def forward(self, wav):
101
+ batch, streams, channels, time = wav.size()
102
+ device = wav.device
103
+ if self.training and random.random() < self.proba:
104
+ scales = th.empty(batch, streams, 1, 1, device=device).uniform_(self.min, self.max)
105
+ wav *= scales
106
+ return wav
demucs/compressed.py ADDED
@@ -0,0 +1,115 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Facebook, Inc. and its affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import json
8
+ from fractions import Fraction
9
+ from concurrent import futures
10
+
11
+ import musdb
12
+ from torch import distributed
13
+
14
+ from .audio import AudioFile
15
+
16
+
17
+ def get_musdb_tracks(root, *args, **kwargs):
18
+ mus = musdb.DB(root, *args, **kwargs)
19
+ return {track.name: track.path for track in mus}
20
+
21
+
22
+ class StemsSet:
23
+ def __init__(self, tracks, metadata, duration=None, stride=1,
24
+ samplerate=44100, channels=2, streams=slice(None)):
25
+
26
+ self.metadata = []
27
+ for name, path in tracks.items():
28
+ meta = dict(metadata[name])
29
+ meta["path"] = path
30
+ meta["name"] = name
31
+ self.metadata.append(meta)
32
+ if duration is not None and meta["duration"] < duration:
33
+ raise ValueError(f"Track {name} duration is too small {meta['duration']}")
34
+ self.metadata.sort(key=lambda x: x["name"])
35
+ self.duration = duration
36
+ self.stride = stride
37
+ self.channels = channels
38
+ self.samplerate = samplerate
39
+ self.streams = streams
40
+
41
+ def __len__(self):
42
+ return sum(self._examples_count(m) for m in self.metadata)
43
+
44
+ def _examples_count(self, meta):
45
+ if self.duration is None:
46
+ return 1
47
+ else:
48
+ return int((meta["duration"] - self.duration) // self.stride + 1)
49
+
50
+ def track_metadata(self, index):
51
+ for meta in self.metadata:
52
+ examples = self._examples_count(meta)
53
+ if index >= examples:
54
+ index -= examples
55
+ continue
56
+ return meta
57
+
58
+ def __getitem__(self, index):
59
+ for meta in self.metadata:
60
+ examples = self._examples_count(meta)
61
+ if index >= examples:
62
+ index -= examples
63
+ continue
64
+ streams = AudioFile(meta["path"]).read(seek_time=index * self.stride,
65
+ duration=self.duration,
66
+ channels=self.channels,
67
+ samplerate=self.samplerate,
68
+ streams=self.streams)
69
+ return (streams - meta["mean"]) / meta["std"]
70
+
71
+
72
+ def _get_track_metadata(path):
73
+ # use mono at 44kHz as reference. For any other settings data won't be perfectly
74
+ # normalized but it should be good enough.
75
+ audio = AudioFile(path)
76
+ mix = audio.read(streams=0, channels=1, samplerate=44100)
77
+ return {"duration": audio.duration, "std": mix.std().item(), "mean": mix.mean().item()}
78
+
79
+
80
+ def _build_metadata(tracks, workers=10):
81
+ pendings = []
82
+ with futures.ProcessPoolExecutor(workers) as pool:
83
+ for name, path in tracks.items():
84
+ pendings.append((name, pool.submit(_get_track_metadata, path)))
85
+ return {name: p.result() for name, p in pendings}
86
+
87
+
88
+ def _build_musdb_metadata(path, musdb, workers):
89
+ tracks = get_musdb_tracks(musdb)
90
+ metadata = _build_metadata(tracks, workers)
91
+ path.parent.mkdir(exist_ok=True, parents=True)
92
+ json.dump(metadata, open(path, "w"))
93
+
94
+
95
+ def get_compressed_datasets(args, samples):
96
+ metadata_file = args.metadata / "musdb.json"
97
+ if not metadata_file.is_file() and args.rank == 0:
98
+ _build_musdb_metadata(metadata_file, args.musdb, args.workers)
99
+ if args.world_size > 1:
100
+ distributed.barrier()
101
+ metadata = json.load(open(metadata_file))
102
+ duration = Fraction(samples, args.samplerate)
103
+ stride = Fraction(args.data_stride, args.samplerate)
104
+ train_set = StemsSet(get_musdb_tracks(args.musdb, subsets=["train"], split="train"),
105
+ metadata,
106
+ duration=duration,
107
+ stride=stride,
108
+ streams=slice(1, None),
109
+ samplerate=args.samplerate,
110
+ channels=args.audio_channels)
111
+ valid_set = StemsSet(get_musdb_tracks(args.musdb, subsets=["train"], split="valid"),
112
+ metadata,
113
+ samplerate=args.samplerate,
114
+ channels=args.audio_channels)
115
+ return train_set, valid_set
demucs/model.py ADDED
@@ -0,0 +1,202 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Facebook, Inc. and its affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import math
8
+
9
+ import julius
10
+ from torch import nn
11
+
12
+ from .utils import capture_init, center_trim
13
+
14
+
15
+ class BLSTM(nn.Module):
16
+ def __init__(self, dim, layers=1):
17
+ super().__init__()
18
+ self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
19
+ self.linear = nn.Linear(2 * dim, dim)
20
+
21
+ def forward(self, x):
22
+ x = x.permute(2, 0, 1)
23
+ x = self.lstm(x)[0]
24
+ x = self.linear(x)
25
+ x = x.permute(1, 2, 0)
26
+ return x
27
+
28
+
29
+ def rescale_conv(conv, reference):
30
+ std = conv.weight.std().detach()
31
+ scale = (std / reference)**0.5
32
+ conv.weight.data /= scale
33
+ if conv.bias is not None:
34
+ conv.bias.data /= scale
35
+
36
+
37
+ def rescale_module(module, reference):
38
+ for sub in module.modules():
39
+ if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)):
40
+ rescale_conv(sub, reference)
41
+
42
+
43
+ class Demucs(nn.Module):
44
+ @capture_init
45
+ def __init__(self,
46
+ sources,
47
+ audio_channels=2,
48
+ channels=64,
49
+ depth=6,
50
+ rewrite=True,
51
+ glu=True,
52
+ rescale=0.1,
53
+ resample=True,
54
+ kernel_size=8,
55
+ stride=4,
56
+ growth=2.,
57
+ lstm_layers=2,
58
+ context=3,
59
+ normalize=False,
60
+ samplerate=44100,
61
+ segment_length=4 * 10 * 44100):
62
+ """
63
+ Args:
64
+ sources (list[str]): list of source names
65
+ audio_channels (int): stereo or mono
66
+ channels (int): first convolution channels
67
+ depth (int): number of encoder/decoder layers
68
+ rewrite (bool): add 1x1 convolution to each encoder layer
69
+ and a convolution to each decoder layer.
70
+ For the decoder layer, `context` gives the kernel size.
71
+ glu (bool): use glu instead of ReLU
72
+ resample_input (bool): upsample x2 the input and downsample /2 the output.
73
+ rescale (int): rescale initial weights of convolutions
74
+ to get their standard deviation closer to `rescale`
75
+ kernel_size (int): kernel size for convolutions
76
+ stride (int): stride for convolutions
77
+ growth (float): multiply (resp divide) number of channels by that
78
+ for each layer of the encoder (resp decoder)
79
+ lstm_layers (int): number of lstm layers, 0 = no lstm
80
+ context (int): kernel size of the convolution in the
81
+ decoder before the transposed convolution. If > 1,
82
+ will provide some context from neighboring time
83
+ steps.
84
+ samplerate (int): stored as meta information for easing
85
+ future evaluations of the model.
86
+ segment_length (int): stored as meta information for easing
87
+ future evaluations of the model. Length of the segments on which
88
+ the model was trained.
89
+ """
90
+
91
+ super().__init__()
92
+ self.audio_channels = audio_channels
93
+ self.sources = sources
94
+ self.kernel_size = kernel_size
95
+ self.context = context
96
+ self.stride = stride
97
+ self.depth = depth
98
+ self.resample = resample
99
+ self.channels = channels
100
+ self.normalize = normalize
101
+ self.samplerate = samplerate
102
+ self.segment_length = segment_length
103
+
104
+ self.encoder = nn.ModuleList()
105
+ self.decoder = nn.ModuleList()
106
+
107
+ if glu:
108
+ activation = nn.GLU(dim=1)
109
+ ch_scale = 2
110
+ else:
111
+ activation = nn.ReLU()
112
+ ch_scale = 1
113
+ in_channels = audio_channels
114
+ for index in range(depth):
115
+ encode = []
116
+ encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), nn.ReLU()]
117
+ if rewrite:
118
+ encode += [nn.Conv1d(channels, ch_scale * channels, 1), activation]
119
+ self.encoder.append(nn.Sequential(*encode))
120
+
121
+ decode = []
122
+ if index > 0:
123
+ out_channels = in_channels
124
+ else:
125
+ out_channels = len(self.sources) * audio_channels
126
+ if rewrite:
127
+ decode += [nn.Conv1d(channels, ch_scale * channels, context), activation]
128
+ decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride)]
129
+ if index > 0:
130
+ decode.append(nn.ReLU())
131
+ self.decoder.insert(0, nn.Sequential(*decode))
132
+ in_channels = channels
133
+ channels = int(growth * channels)
134
+
135
+ channels = in_channels
136
+
137
+ if lstm_layers:
138
+ self.lstm = BLSTM(channels, lstm_layers)
139
+ else:
140
+ self.lstm = None
141
+
142
+ if rescale:
143
+ rescale_module(self, reference=rescale)
144
+
145
+ def valid_length(self, length):
146
+ """
147
+ Return the nearest valid length to use with the model so that
148
+ there is no time steps left over in a convolutions, e.g. for all
149
+ layers, size of the input - kernel_size % stride = 0.
150
+
151
+ If the mixture has a valid length, the estimated sources
152
+ will have exactly the same length when context = 1. If context > 1,
153
+ the two signals can be center trimmed to match.
154
+
155
+ For training, extracts should have a valid length.For evaluation
156
+ on full tracks we recommend passing `pad = True` to :method:`forward`.
157
+ """
158
+ if self.resample:
159
+ length *= 2
160
+ for _ in range(self.depth):
161
+ length = math.ceil((length - self.kernel_size) / self.stride) + 1
162
+ length = max(1, length)
163
+ length += self.context - 1
164
+ for _ in range(self.depth):
165
+ length = (length - 1) * self.stride + self.kernel_size
166
+
167
+ if self.resample:
168
+ length = math.ceil(length / 2)
169
+ return int(length)
170
+
171
+ def forward(self, mix):
172
+ x = mix
173
+
174
+ if self.normalize:
175
+ mono = mix.mean(dim=1, keepdim=True)
176
+ mean = mono.mean(dim=-1, keepdim=True)
177
+ std = mono.std(dim=-1, keepdim=True)
178
+ else:
179
+ mean = 0
180
+ std = 1
181
+
182
+ x = (x - mean) / (1e-5 + std)
183
+
184
+ if self.resample:
185
+ x = julius.resample_frac(x, 1, 2)
186
+
187
+ saved = []
188
+ for encode in self.encoder:
189
+ x = encode(x)
190
+ saved.append(x)
191
+ if self.lstm:
192
+ x = self.lstm(x)
193
+ for decode in self.decoder:
194
+ skip = center_trim(saved.pop(-1), x)
195
+ x = x + skip
196
+ x = decode(x)
197
+
198
+ if self.resample:
199
+ x = julius.resample_frac(x, 2, 1)
200
+ x = x * std + mean
201
+ x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1))
202
+ return x
demucs/parser.py ADDED
@@ -0,0 +1,244 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Facebook, Inc. and its affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import argparse
8
+ import os
9
+ from pathlib import Path
10
+
11
+
12
+ def get_parser():
13
+ parser = argparse.ArgumentParser("demucs", description="Train and evaluate Demucs.")
14
+ default_raw = None
15
+ default_musdb = None
16
+ if 'DEMUCS_RAW' in os.environ:
17
+ default_raw = Path(os.environ['DEMUCS_RAW'])
18
+ if 'DEMUCS_MUSDB' in os.environ:
19
+ default_musdb = Path(os.environ['DEMUCS_MUSDB'])
20
+ parser.add_argument(
21
+ "--raw",
22
+ type=Path,
23
+ default=default_raw,
24
+ help="Path to raw audio, can be faster, see python3 -m demucs.raw to extract.")
25
+ parser.add_argument("--no_raw", action="store_const", const=None, dest="raw")
26
+ parser.add_argument("-m",
27
+ "--musdb",
28
+ type=Path,
29
+ default=default_musdb,
30
+ help="Path to musdb root")
31
+ parser.add_argument("--is_wav", action="store_true",
32
+ help="Indicate that the MusDB dataset is in wav format (i.e. MusDB-HQ).")
33
+ parser.add_argument("--metadata", type=Path, default=Path("metadata/"),
34
+ help="Folder where metadata information is stored.")
35
+ parser.add_argument("--wav", type=Path,
36
+ help="Path to a wav dataset. This should contain a 'train' and a 'valid' "
37
+ "subfolder.")
38
+ parser.add_argument("--samplerate", type=int, default=44100)
39
+ parser.add_argument("--audio_channels", type=int, default=2)
40
+ parser.add_argument("--samples",
41
+ default=44100 * 10,
42
+ type=int,
43
+ help="number of samples to feed in")
44
+ parser.add_argument("--data_stride",
45
+ default=44100,
46
+ type=int,
47
+ help="Stride for chunks, shorter = longer epochs")
48
+ parser.add_argument("-w", "--workers", default=10, type=int, help="Loader workers")
49
+ parser.add_argument("--eval_workers", default=2, type=int, help="Final evaluation workers")
50
+ parser.add_argument("-d",
51
+ "--device",
52
+ help="Device to train on, default is cuda if available else cpu")
53
+ parser.add_argument("--eval_cpu", action="store_true", help="Eval on test will be run on cpu.")
54
+ parser.add_argument("--dummy", help="Dummy parameter, useful to create a new checkpoint file")
55
+ parser.add_argument("--test", help="Just run the test pipeline + one validation. "
56
+ "This should be a filename relative to the models/ folder.")
57
+ parser.add_argument("--test_pretrained", help="Just run the test pipeline + one validation, "
58
+ "on a pretrained model. ")
59
+
60
+ parser.add_argument("--rank", default=0, type=int)
61
+ parser.add_argument("--world_size", default=1, type=int)
62
+ parser.add_argument("--master")
63
+
64
+ parser.add_argument("--checkpoints",
65
+ type=Path,
66
+ default=Path("checkpoints"),
67
+ help="Folder where to store checkpoints etc")
68
+ parser.add_argument("--evals",
69
+ type=Path,
70
+ default=Path("evals"),
71
+ help="Folder where to store evals and waveforms")
72
+ parser.add_argument("--save",
73
+ action="store_true",
74
+ help="Save estimated for the test set waveforms")
75
+ parser.add_argument("--logs",
76
+ type=Path,
77
+ default=Path("logs"),
78
+ help="Folder where to store logs")
79
+ parser.add_argument("--models",
80
+ type=Path,
81
+ default=Path("models"),
82
+ help="Folder where to store trained models")
83
+ parser.add_argument("-R",
84
+ "--restart",
85
+ action='store_true',
86
+ help='Restart training, ignoring previous run')
87
+
88
+ parser.add_argument("--seed", type=int, default=42)
89
+ parser.add_argument("-e", "--epochs", type=int, default=180, help="Number of epochs")
90
+ parser.add_argument("-r",
91
+ "--repeat",
92
+ type=int,
93
+ default=2,
94
+ help="Repeat the train set, longer epochs")
95
+ parser.add_argument("-b", "--batch_size", type=int, default=64)
96
+ parser.add_argument("--lr", type=float, default=3e-4)
97
+ parser.add_argument("--mse", action="store_true", help="Use MSE instead of L1")
98
+ parser.add_argument("--init", help="Initialize from a pre-trained model.")
99
+
100
+ # Augmentation options
101
+ parser.add_argument("--no_augment",
102
+ action="store_false",
103
+ dest="augment",
104
+ default=True,
105
+ help="No basic data augmentation.")
106
+ parser.add_argument("--repitch", type=float, default=0.2,
107
+ help="Probability to do tempo/pitch change")
108
+ parser.add_argument("--max_tempo", type=float, default=12,
109
+ help="Maximum relative tempo change in %% when using repitch.")
110
+
111
+ parser.add_argument("--remix_group_size",
112
+ type=int,
113
+ default=4,
114
+ help="Shuffle sources using group of this size. Useful to somewhat "
115
+ "replicate multi-gpu training "
116
+ "on less GPUs.")
117
+ parser.add_argument("--shifts",
118
+ type=int,
119
+ default=10,
120
+ help="Number of random shifts used for the shift trick.")
121
+ parser.add_argument("--overlap",
122
+ type=float,
123
+ default=0.25,
124
+ help="Overlap when --split_valid is passed.")
125
+
126
+ # See model.py for doc
127
+ parser.add_argument("--growth",
128
+ type=float,
129
+ default=2.,
130
+ help="Number of channels between two layers will increase by this factor")
131
+ parser.add_argument("--depth",
132
+ type=int,
133
+ default=6,
134
+ help="Number of layers for the encoder and decoder")
135
+ parser.add_argument("--lstm_layers", type=int, default=2, help="Number of layers for the LSTM")
136
+ parser.add_argument("--channels",
137
+ type=int,
138
+ default=64,
139
+ help="Number of channels for the first encoder layer")
140
+ parser.add_argument("--kernel_size",
141
+ type=int,
142
+ default=8,
143
+ help="Kernel size for the (transposed) convolutions")
144
+ parser.add_argument("--conv_stride",
145
+ type=int,
146
+ default=4,
147
+ help="Stride for the (transposed) convolutions")
148
+ parser.add_argument("--context",
149
+ type=int,
150
+ default=3,
151
+ help="Context size for the decoder convolutions "
152
+ "before the transposed convolutions")
153
+ parser.add_argument("--rescale",
154
+ type=float,
155
+ default=0.1,
156
+ help="Initial weight rescale reference")
157
+ parser.add_argument("--no_resample", action="store_false",
158
+ default=True, dest="resample",
159
+ help="No Resampling of the input/output x2")
160
+ parser.add_argument("--no_glu",
161
+ action="store_false",
162
+ default=True,
163
+ dest="glu",
164
+ help="Replace all GLUs by ReLUs")
165
+ parser.add_argument("--no_rewrite",
166
+ action="store_false",
167
+ default=True,
168
+ dest="rewrite",
169
+ help="No 1x1 rewrite convolutions")
170
+ parser.add_argument("--normalize", action="store_true")
171
+ parser.add_argument("--no_norm_wav", action="store_false", dest='norm_wav', default=True)
172
+
173
+ # Tasnet options
174
+ parser.add_argument("--tasnet", action="store_true")
175
+ parser.add_argument("--split_valid",
176
+ action="store_true",
177
+ help="Predict chunks by chunks for valid and test. Required for tasnet")
178
+ parser.add_argument("--X", type=int, default=8)
179
+
180
+ # Other options
181
+ parser.add_argument("--show",
182
+ action="store_true",
183
+ help="Show model architecture, size and exit")
184
+ parser.add_argument("--save_model", action="store_true",
185
+ help="Skip traning, just save final model "
186
+ "for the current checkpoint value.")
187
+ parser.add_argument("--save_state",
188
+ help="Skip training, just save state "
189
+ "for the current checkpoint value. You should "
190
+ "provide a model name as argument.")
191
+
192
+ # Quantization options
193
+ parser.add_argument("--q-min-size", type=float, default=1,
194
+ help="Only quantize layers over this size (in MB)")
195
+ parser.add_argument(
196
+ "--qat", type=int, help="If provided, use QAT training with that many bits.")
197
+
198
+ parser.add_argument("--diffq", type=float, default=0)
199
+ parser.add_argument(
200
+ "--ms-target", type=float, default=162,
201
+ help="Model size target in MB, when using DiffQ. Best model will be kept "
202
+ "only if it is smaller than this target.")
203
+
204
+ return parser
205
+
206
+
207
+ def get_name(parser, args):
208
+ """
209
+ Return the name of an experiment given the args. Some parameters are ignored,
210
+ for instance --workers, as they do not impact the final result.
211
+ """
212
+ ignore_args = set([
213
+ "checkpoints",
214
+ "deterministic",
215
+ "eval",
216
+ "evals",
217
+ "eval_cpu",
218
+ "eval_workers",
219
+ "logs",
220
+ "master",
221
+ "rank",
222
+ "restart",
223
+ "save",
224
+ "save_model",
225
+ "save_state",
226
+ "show",
227
+ "workers",
228
+ "world_size",
229
+ ])
230
+ parts = []
231
+ name_args = dict(args.__dict__)
232
+ for name, value in name_args.items():
233
+ if name in ignore_args:
234
+ continue
235
+ if value != parser.get_default(name):
236
+ if isinstance(value, Path):
237
+ parts.append(f"{name}={value.name}")
238
+ else:
239
+ parts.append(f"{name}={value}")
240
+ if parts:
241
+ name = " ".join(parts)
242
+ else:
243
+ name = "default"
244
+ return name