File size: 55,334 Bytes
7bc29af
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
18c71e6
 
 
 
 
 
 
 
 
 
 
 
7bc29af
 
 
 
18c71e6
7bc29af
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
18c71e6
 
7bc29af
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
import subprocess
import os
import sys
import errno
import shutil
import yt_dlp
from mega import Mega
import datetime
import unicodedata
import torch
import glob
import gradio as gr
import gdown
import zipfile
import traceback
import json
import mdx
from mdx_processing_script import get_model_list,id_to_ptm,prepare_mdx,run_mdx
import requests
import wget
import ffmpeg
import hashlib
now_dir = os.getcwd()
sys.path.append(now_dir)
from unidecode import unidecode
import re
import time
from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
from infer.modules.vc.pipeline import Pipeline
VC = Pipeline
from lib.infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono,
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono,
)
from MDXNet import MDXNetDereverb
from configs.config import Config
from infer_uvr5 import _audio_pre_, _audio_pre_new
from huggingface_hub import HfApi, list_models
from huggingface_hub import login
from i18n import I18nAuto
i18n = I18nAuto()
from bs4 import BeautifulSoup
from sklearn.cluster import MiniBatchKMeans
from dotenv import load_dotenv
load_dotenv()
config = Config()
tmp = os.path.join(now_dir, "TEMP")
shutil.rmtree(tmp, ignore_errors=True)
os.environ["TEMP"] = tmp
weight_root = os.getenv("weight_root")
weight_uvr5_root = os.getenv("weight_uvr5_root")
index_root = os.getenv("index_root")
audio_root = "audios"
names = []
for name in os.listdir(weight_root):
    if name.endswith(".pth"):
        names.append(name)
index_paths = []

global indexes_list
indexes_list = []

audio_paths = []
for root, dirs, files in os.walk(index_root, topdown=False):
    for name in files:
        if name.endswith(".index") and "trained" not in name:
            index_paths.append("%s\\%s" % (root, name))

for root, dirs, files in os.walk(audio_root, topdown=False):
    for name in files:
        audio_paths.append("%s/%s" % (root, name))

uvr5_names = []
for name in os.listdir(weight_uvr5_root):
    if name.endswith(".pth") or "onnx" in name:
        uvr5_names.append(name.replace(".pth", ""))

def calculate_md5(file_path):
    hash_md5 = hashlib.md5()
    with open(file_path, "rb") as f:
        for chunk in iter(lambda: f.read(4096), b""):
            hash_md5.update(chunk)
    return hash_md5.hexdigest()

def format_title(title):
     formatted_title = re.sub(r'[^\w\s-]', '', title)
     formatted_title = formatted_title.replace(" ", "_")
     return formatted_title

def silentremove(filename):
    try:
        os.remove(filename)
    except OSError as e: 
        if e.errno != errno.ENOENT: 
            raise 
def get_md5(temp_folder):
  for root, subfolders, files in os.walk(temp_folder):
    for file in files:
      if not file.startswith("G_") and not file.startswith("D_") and file.endswith(".pth") and not "_G_" in file and not "_D_" in file:
        md5_hash = calculate_md5(os.path.join(root, file))
        return md5_hash

  return None

def find_parent(search_dir, file_name):
    for dirpath, dirnames, filenames in os.walk(search_dir):
        if file_name in filenames:
            return os.path.abspath(dirpath)
    return None

def find_folder_parent(search_dir, folder_name):
    for dirpath, dirnames, filenames in os.walk(search_dir):
        if folder_name in dirnames:
            return os.path.abspath(dirpath)
    return None


def delete_large_files(directory_path, max_size_megabytes):
    for filename in os.listdir(directory_path):
        file_path = os.path.join(directory_path, filename)
        if os.path.isfile(file_path):
            size_in_bytes = os.path.getsize(file_path)
            size_in_megabytes = size_in_bytes / (1024 * 1024)  # Convert bytes to megabytes

            if size_in_megabytes > max_size_megabytes:
                print("###################################")
                print(f"Deleting s*** {filename} (Size: {size_in_megabytes:.2f} MB)")
                os.remove(file_path)
                print("###################################")    

def download_from_url(url):
    parent_path = find_folder_parent(".", "pretrained_v2")
    zips_path = os.path.join(parent_path, 'zips')
    print(f"Limit download size in MB {os.getenv('MAX_DOWNLOAD_SIZE')}, duplicate the space for modify the limit")
    
    if url != '':
        print(i18n("Downloading the file: ") + f"{url}")
        if "drive.google.com" in url:
            if "file/d/" in url:
                file_id = url.split("file/d/")[1].split("/")[0]
            elif "id=" in url:
                file_id = url.split("id=")[1].split("&")[0]
            else:
                return None
            
            if file_id:
                os.chdir('./zips')
                result = subprocess.run(["gdown", f"https://drive.google.com/uc?id={file_id}", "--fuzzy"], capture_output=True, text=True, encoding='utf-8')
                if "Too many users have viewed or downloaded this file recently" in str(result.stderr):
                    return "too much use"
                if "Cannot retrieve the public link of the file." in str(result.stderr):
                    return "private link"
                print(result.stderr)
                
        elif "/blob/" in url:
            os.chdir('./zips')
            url = url.replace("blob", "resolve")
            response = requests.get(url)
            if response.status_code == 200:
                file_name = url.split('/')[-1]
                with open(os.path.join(zips_path, file_name), "wb") as newfile:
                    newfile.write(response.content)
            else:
                    os.chdir(parent_path)
        elif "mega.nz" in url:
            if "#!" in url:
                file_id = url.split("#!")[1].split("!")[0]
            elif "file/" in url:
                file_id = url.split("file/")[1].split("/")[0]
            else:
                return None
            if file_id:
                m = Mega()
                m.download_url(url, zips_path)
        elif "/tree/main" in url:
           response = requests.get(url)
           soup = BeautifulSoup(response.content, 'html.parser')
           temp_url = ''
           for link in soup.find_all('a', href=True):
               if link['href'].endswith('.zip'):
                  temp_url = link['href']
                  break
           if temp_url:
              url = temp_url
              url = url.replace("blob", "resolve")
              if "huggingface.co" not in url:
                 url = "https://huggingface.co" + url

                 wget.download(url)
           else:
                 print("No .zip file found on the page.")
        elif "cdn.discordapp.com" in url:
            file = requests.get(url)
            if file.status_code == 200:
                name = url.split('/')
                with open(os.path.join(zips_path, name[len(name)-1]), "wb") as newfile:
                    newfile.write(file.content)
            else:
                return None
        elif "pixeldrain.com" in url:
            try:
                file_id = url.split("pixeldrain.com/u/")[1]
                os.chdir('./zips')
                print(file_id)
                response = requests.get(f"https://pixeldrain.com/api/file/{file_id}")
                if response.status_code == 200:
                    file_name = response.headers.get("Content-Disposition").split('filename=')[-1].strip('";')
                    if not os.path.exists(zips_path):
                        os.makedirs(zips_path)
                    with open(os.path.join(zips_path, file_name), "wb") as newfile:
                        newfile.write(response.content)
                        os.chdir(parent_path)
                        return "downloaded"
                else:
                    os.chdir(parent_path)
                    return None
            except Exception as e:
                print(e)
                os.chdir(parent_path)
                return None
        else:
            os.chdir('./zips')
            wget.download(url)
            
        #os.chdir('./zips')    
        delete_large_files(zips_path, int(os.getenv("MAX_DOWNLOAD_SIZE")))    
        os.chdir(parent_path)
        print(i18n("Full download"))
        return "downloaded"
    else:
        return None
                
class error_message(Exception):
    def __init__(self, mensaje):
        self.mensaje = mensaje
        super().__init__(mensaje)

def get_vc(sid, to_return_protect0, to_return_protect1):
    global n_spk, tgt_sr, net_g, vc, cpt, version
    if sid == "" or sid == []:
        global hubert_model
        if hubert_model is not None: 
            print("clean_empty_cache")
            del net_g, n_spk, vc, hubert_model, tgt_sr 
            hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
            if_f0 = cpt.get("f0", 1)
            version = cpt.get("version", "v1")
            if version == "v1":
                if if_f0 == 1:
                    net_g = SynthesizerTrnMs256NSFsid(
                        *cpt["config"], is_half=config.is_half
                    )
                else:
                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
            elif version == "v2":
                if if_f0 == 1:
                    net_g = SynthesizerTrnMs768NSFsid(
                        *cpt["config"], is_half=config.is_half
                    )
                else:
                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
            del net_g, cpt
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
            cpt = None
        return (
            {"visible": False, "__type__": "update"},
            {"visible": False, "__type__": "update"},
            {"visible": False, "__type__": "update"},
        )
    person = "%s/%s" % (weight_root, sid)
    print("loading %s" % person)
    cpt = torch.load(person, map_location="cpu")
    tgt_sr = cpt["config"][-1]
    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  
    if_f0 = cpt.get("f0", 1)
    if if_f0 == 0:
        to_return_protect0 = to_return_protect1 = {
            "visible": False,
            "value": 0.5,
            "__type__": "update",
        }
    else:
        to_return_protect0 = {
            "visible": True,
            "value": to_return_protect0,
            "__type__": "update",
        }
        to_return_protect1 = {
            "visible": True,
            "value": to_return_protect1,
            "__type__": "update",
        }
    version = cpt.get("version", "v1")
    if version == "v1":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
        else:
            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
    elif version == "v2":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
        else:
            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
    del net_g.enc_q
    print(net_g.load_state_dict(cpt["weight"], strict=False))
    net_g.eval().to(config.device)
    if config.is_half:
        net_g = net_g.half()
    else:
        net_g = net_g.float()
    vc = VC(tgt_sr, config)
    n_spk = cpt["config"][-3]
    return (
        {"visible": True, "maximum": n_spk, "__type__": "update"},
        to_return_protect0,
        to_return_protect1,
    )
        
def load_downloaded_model(url):
    parent_path = find_folder_parent(".", "pretrained_v2")
    try:
        infos = []
        logs_folders = ['0_gt_wavs','1_16k_wavs','2a_f0','2b-f0nsf','3_feature256','3_feature768']
        zips_path = os.path.join(parent_path, 'zips')
        unzips_path = os.path.join(parent_path, 'unzips')
        weights_path = os.path.join(parent_path, 'weights')
        logs_dir = ""
        
        if os.path.exists(zips_path):
            shutil.rmtree(zips_path)
        if os.path.exists(unzips_path):
            shutil.rmtree(unzips_path)

        os.mkdir(zips_path)
        os.mkdir(unzips_path)
        
        download_file = download_from_url(url)
        if not download_file:
            print(i18n("The file could not be downloaded."))
            infos.append(i18n("The file could not be downloaded."))
            yield "\n".join(infos)
        elif download_file == "downloaded":
            print(i18n("It has been downloaded successfully."))
            infos.append(i18n("It has been downloaded successfully."))
            yield "\n".join(infos)
        elif download_file == "too much use":
            raise Exception(i18n("Too many users have recently viewed or downloaded this file"))
        elif download_file == "private link":
            raise Exception(i18n("Cannot get file from this private link"))
        
        for filename in os.listdir(zips_path):
            if filename.endswith(".zip"):
                zipfile_path = os.path.join(zips_path,filename)
                print(i18n("Proceeding with the extraction..."))
                infos.append(i18n("Proceeding with the extraction..."))
                shutil.unpack_archive(zipfile_path, unzips_path, 'zip')
                model_name = os.path.basename(zipfile_path)
                logs_dir = os.path.join(parent_path,'logs', os.path.normpath(str(model_name).replace(".zip","")))
                yield "\n".join(infos)
            else:
                print(i18n("Unzip error."))
                infos.append(i18n("Unzip error."))
                yield "\n".join(infos)
        
        index_file = False
        model_file = False
        D_file = False
        G_file = False
        
        for path, subdirs, files in os.walk(unzips_path):
            for item in files:
                item_path = os.path.join(path, item)
                if not 'G_' in item and not 'D_' in item and item.endswith('.pth'):
                    model_file = True
                    model_name = item.replace(".pth","")
                    logs_dir = os.path.join(parent_path,'logs', model_name)
                    if os.path.exists(logs_dir):
                        shutil.rmtree(logs_dir)
                    os.mkdir(logs_dir)
                    if not os.path.exists(weights_path):
                        os.mkdir(weights_path)
                    if os.path.exists(os.path.join(weights_path, item)):
                        os.remove(os.path.join(weights_path, item))
                    if os.path.exists(item_path):
                        shutil.move(item_path, weights_path)
        
        if not model_file and not os.path.exists(logs_dir):
            os.mkdir(logs_dir)
        for path, subdirs, files in os.walk(unzips_path):
            for item in files:
                item_path = os.path.join(path, item)
                if item.startswith('added_') and item.endswith('.index'):
                    index_file = True
                    if os.path.exists(item_path):
                        if os.path.exists(os.path.join(logs_dir, item)):
                            os.remove(os.path.join(logs_dir, item))
                        shutil.move(item_path, logs_dir)
                if item.startswith('total_fea.npy') or item.startswith('events.'):
                    if os.path.exists(item_path):
                        if os.path.exists(os.path.join(logs_dir, item)):
                            os.remove(os.path.join(logs_dir, item))
                        shutil.move(item_path, logs_dir)
        
                
        result = ""
        if model_file:
            if index_file:
                print(i18n("The model works for inference, and has the .index file."))
                infos.append("\n" + i18n("The model works for inference, and has the .index file."))
                yield "\n".join(infos)
            else:
                print(i18n("The model works for inference, but it doesn't have the .index file."))
                infos.append("\n" + i18n("The model works for inference, but it doesn't have the .index file."))
                yield "\n".join(infos)
        
        if not index_file and not model_file:
            print(i18n("No relevant file was found to upload."))
            infos.append(i18n("No relevant file was found to upload."))
            yield "\n".join(infos)
        
        if os.path.exists(zips_path):
            shutil.rmtree(zips_path)
        if os.path.exists(unzips_path):
            shutil.rmtree(unzips_path)
        os.chdir(parent_path)    
        return result
    except Exception as e:
        os.chdir(parent_path)
        if "too much use" in str(e):
            print(i18n("Too many users have recently viewed or downloaded this file"))
            yield i18n("Too many users have recently viewed or downloaded this file")
        elif "private link" in str(e):
            print(i18n("Cannot get file from this private link"))
            yield i18n("Cannot get file from this private link")
        else:
            print(e)
            yield i18n("An error occurred downloading")
    finally:
        os.chdir(parent_path)
      
def load_dowloaded_dataset(url):
    parent_path = find_folder_parent(".", "pretrained_v2")
    infos = []
    try:
        zips_path = os.path.join(parent_path, 'zips')
        unzips_path = os.path.join(parent_path, 'unzips')
        datasets_path = os.path.join(parent_path, 'datasets')
        audio_extenions =['wav', 'mp3', 'flac', 'ogg', 'opus',
                'm4a', 'mp4', 'aac', 'alac', 'wma',
                'aiff', 'webm', 'ac3']
        
        if os.path.exists(zips_path):
            shutil.rmtree(zips_path)
        if os.path.exists(unzips_path):
            shutil.rmtree(unzips_path)
            
        if not os.path.exists(datasets_path):
            os.mkdir(datasets_path)
            
        os.mkdir(zips_path)
        os.mkdir(unzips_path)
        
        download_file = download_from_url(url)
        
        if not download_file:
            print(i18n("An error occurred downloading"))
            infos.append(i18n("An error occurred downloading"))
            yield "\n".join(infos)
            raise Exception(i18n("An error occurred downloading"))
        elif download_file == "downloaded":
            print(i18n("It has been downloaded successfully."))
            infos.append(i18n("It has been downloaded successfully."))
            yield "\n".join(infos)
        elif download_file == "too much use":
            raise Exception(i18n("Too many users have recently viewed or downloaded this file"))
        elif download_file == "private link":
            raise Exception(i18n("Cannot get file from this private link"))
  
        zip_path = os.listdir(zips_path)
        foldername = ""
        for file in zip_path:
            if file.endswith('.zip'):
                file_path = os.path.join(zips_path, file)
                print("....")
                foldername = file.replace(".zip","").replace(" ","").replace("-","_")
                dataset_path = os.path.join(datasets_path, foldername)
                print(i18n("Proceeding with the extraction..."))
                infos.append(i18n("Proceeding with the extraction..."))
                yield "\n".join(infos)
                shutil.unpack_archive(file_path, unzips_path, 'zip')
                if os.path.exists(dataset_path):
                    shutil.rmtree(dataset_path)
                    
                os.mkdir(dataset_path)
                
                for root, subfolders, songs in os.walk(unzips_path):
                    for song in songs:
                        song_path = os.path.join(root, song)
                        if song.endswith(tuple(audio_extenions)):
                            formatted_song_name = format_title(os.path.splitext(song)[0])
                            extension = os.path.splitext(song)[1]
                            new_song_path = os.path.join(dataset_path, f"{formatted_song_name}{extension}")
                            shutil.move(song_path, new_song_path)
            else:
                print(i18n("Unzip error."))
                infos.append(i18n("Unzip error."))
                yield "\n".join(infos)
                
                

        if os.path.exists(zips_path):
            shutil.rmtree(zips_path)
        if os.path.exists(unzips_path):
            shutil.rmtree(unzips_path)
            
        print(i18n("The Dataset has been loaded successfully."))
        infos.append(i18n("The Dataset has been loaded successfully."))
        yield "\n".join(infos)
    except Exception as e:
        os.chdir(parent_path)
        if "too much use" in str(e):
            print(i18n("Too many users have recently viewed or downloaded this file"))
            yield i18n("Too many users have recently viewed or downloaded this file")   
        elif "private link" in str(e):
            print(i18n("Cannot get file from this private link"))
            yield i18n("Cannot get file from this private link")
        else:
            print(e)
            yield i18n("An error occurred downloading")
    finally:
        os.chdir(parent_path)

def save_model(modelname, save_action):
       
    parent_path = find_folder_parent(".", "pretrained_v2")
    zips_path = os.path.join(parent_path, 'zips')
    dst = os.path.join(zips_path,modelname)
    logs_path = os.path.join(parent_path, 'logs', modelname)
    weights_path = os.path.join(parent_path, 'weights', f"{modelname}.pth")
    save_folder = parent_path
    infos = []    
    
    try:
        if not os.path.exists(logs_path):
            raise Exception("No model found.")
        
        if not 'content' in parent_path:
            save_folder = os.path.join(parent_path, 'RVC_Backup')
        else:
            save_folder = '/content/drive/MyDrive/RVC_Backup'
        
        infos.append(i18n("Save model"))
        yield "\n".join(infos)
        
        if not os.path.exists(save_folder):
            os.mkdir(save_folder)
        if not os.path.exists(os.path.join(save_folder, 'ManualTrainingBackup')):
            os.mkdir(os.path.join(save_folder, 'ManualTrainingBackup'))
        if not os.path.exists(os.path.join(save_folder, 'Finished')):
            os.mkdir(os.path.join(save_folder, 'Finished'))

        if os.path.exists(zips_path):
            shutil.rmtree(zips_path)
            
        os.mkdir(zips_path)
        added_file = glob.glob(os.path.join(logs_path, "added_*.index"))
        d_file = glob.glob(os.path.join(logs_path, "D_*.pth"))
        g_file = glob.glob(os.path.join(logs_path, "G_*.pth"))
        
        if save_action == i18n("Choose the method"):
            raise Exception("No method choosen.")
        
        if save_action == i18n("Save all"):
            print(i18n("Save all"))
            save_folder = os.path.join(save_folder, 'ManualTrainingBackup')
            shutil.copytree(logs_path, dst)
        else:
            if not os.path.exists(dst):
                os.mkdir(dst)
            
        if save_action == i18n("Save D and G"):
            print(i18n("Save D and G"))
            save_folder = os.path.join(save_folder, 'ManualTrainingBackup')
            if len(d_file) > 0:
                shutil.copy(d_file[0], dst)
            if len(g_file) > 0:
                shutil.copy(g_file[0], dst)    
                
            if len(added_file) > 0:
                shutil.copy(added_file[0], dst)
            else:
                infos.append(i18n("Saved without index..."))
                
        if save_action == i18n("Save voice"):
            print(i18n("Save voice"))
            save_folder = os.path.join(save_folder, 'Finished')
            if len(added_file) > 0:
                shutil.copy(added_file[0], dst)
            else:
                infos.append(i18n("Saved without index..."))
        
        yield "\n".join(infos)
        if not os.path.exists(weights_path):
            infos.append(i18n("Saved without inference model..."))
        else:
            shutil.copy(weights_path, dst)
        
        yield "\n".join(infos)
        infos.append("\n" + i18n("This may take a few minutes, please wait..."))
        yield "\n".join(infos)
        
        shutil.make_archive(os.path.join(zips_path,f"{modelname}"), 'zip', zips_path)
        shutil.move(os.path.join(zips_path,f"{modelname}.zip"), os.path.join(save_folder, f'{modelname}.zip'))
        
        shutil.rmtree(zips_path)        
        infos.append("\n" + i18n("Model saved successfully"))
        yield "\n".join(infos)
        
    except Exception as e:
        print(e)
        if "No model found." in str(e):
            infos.append(i18n("The model you want to save does not exist, be sure to enter the correct name."))
        else:
            infos.append(i18n("An error occurred saving the model"))
            
        yield "\n".join(infos)
    
def load_downloaded_backup(url):
    parent_path = find_folder_parent(".", "pretrained_v2")
    try:
        infos = []
        logs_folders = ['0_gt_wavs','1_16k_wavs','2a_f0','2b-f0nsf','3_feature256','3_feature768']
        zips_path = os.path.join(parent_path, 'zips')
        unzips_path = os.path.join(parent_path, 'unzips')
        weights_path = os.path.join(parent_path, 'weights')
        logs_dir = os.path.join(parent_path, 'logs')
        
        if os.path.exists(zips_path):
            shutil.rmtree(zips_path)
        if os.path.exists(unzips_path):
            shutil.rmtree(unzips_path)

        os.mkdir(zips_path)
        os.mkdir(unzips_path)
        
        download_file = download_from_url(url)
        if not download_file:
            print(i18n("The file could not be downloaded."))
            infos.append(i18n("The file could not be downloaded."))
            yield "\n".join(infos)
        elif download_file == "downloaded":
            print(i18n("It has been downloaded successfully."))
            infos.append(i18n("It has been downloaded successfully."))
            yield "\n".join(infos)
        elif download_file == "too much use":
            raise Exception(i18n("Too many users have recently viewed or downloaded this file"))
        elif download_file == "private link":
            raise Exception(i18n("Cannot get file from this private link"))
        
        for filename in os.listdir(zips_path):
            if filename.endswith(".zip"):
                zipfile_path = os.path.join(zips_path,filename)
                zip_dir_name = os.path.splitext(filename)[0]
                unzip_dir = unzips_path
                print(i18n("Proceeding with the extraction..."))
                infos.append(i18n("Proceeding with the extraction..."))
                shutil.unpack_archive(zipfile_path, unzip_dir, 'zip')
                
                if os.path.exists(os.path.join(unzip_dir, zip_dir_name)):
                    shutil.move(os.path.join(unzip_dir, zip_dir_name), logs_dir)
                else:
                    new_folder_path = os.path.join(logs_dir, zip_dir_name)
                    os.mkdir(new_folder_path)
                    for item_name in os.listdir(unzip_dir):
                        item_path = os.path.join(unzip_dir, item_name)
                        if os.path.isfile(item_path):
                            shutil.move(item_path, new_folder_path)
                        elif os.path.isdir(item_path):
                            shutil.move(item_path, new_folder_path)
                    
                yield "\n".join(infos)
            else:
                print(i18n("Unzip error."))
                infos.append(i18n("Unzip error."))
                yield "\n".join(infos)
                
        result = ""
        
        for filename in os.listdir(unzips_path):
            if filename.endswith(".zip"):
                silentremove(filename)
        
        if os.path.exists(zips_path):
            shutil.rmtree(zips_path)
        if os.path.exists(os.path.join(parent_path, 'unzips')):
            shutil.rmtree(os.path.join(parent_path, 'unzips'))
        print(i18n("The Backup has been uploaded successfully."))
        infos.append("\n" + i18n("The Backup has been uploaded successfully."))
        yield "\n".join(infos)
        os.chdir(parent_path)    
        return result
    except Exception as e:
        os.chdir(parent_path)
        if "too much use" in str(e):
            print(i18n("Too many users have recently viewed or downloaded this file"))
            yield i18n("Too many users have recently viewed or downloaded this file")
        elif "private link" in str(e):
            print(i18n("Cannot get file from this private link"))
            yield i18n("Cannot get file from this private link") 
        else:
            print(e)
            yield i18n("An error occurred downloading")
    finally:
        os.chdir(parent_path)

def save_to_wav(record_button):
    if record_button is None:
        pass
    else:
        path_to_file=record_button
        new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav'
        new_path='./audios/'+new_name
        shutil.move(path_to_file,new_path)
        return new_name


def change_choices2():
    audio_paths=[]
    for filename in os.listdir("./audios"):
        if filename.endswith(('wav', 'mp3', 'flac', 'ogg', 'opus',
                'm4a', 'mp4', 'aac', 'alac', 'wma',
                'aiff', 'webm', 'ac3')):
            audio_paths.append(os.path.join('./audios',filename).replace('\\', '/'))
    return {"choices": sorted(audio_paths), "__type__": "update"}, {"__type__": "update"}





def uvr(input_url, output_path, model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0, architecture):
    carpeta_a_eliminar = "yt_downloads"
    if os.path.exists(carpeta_a_eliminar) and os.path.isdir(carpeta_a_eliminar):
        for archivo in os.listdir(carpeta_a_eliminar):
            ruta_archivo = os.path.join(carpeta_a_eliminar, archivo)
            if os.path.isfile(ruta_archivo):
                os.remove(ruta_archivo)
            elif os.path.isdir(ruta_archivo):
                shutil.rmtree(ruta_archivo) 
      
    

    ydl_opts = {
     'no-windows-filenames': True,
     'restrict-filenames': True,
     'extract_audio': True,
     'format': 'bestaudio',
     'quiet': True,
     'no-warnings': True,
     }
    
    try:
        print(i18n("Downloading audio from the video..."))
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
         info_dict = ydl.extract_info(input_url, download=False)
         formatted_title = format_title(info_dict.get('title', 'default_title'))
         formatted_outtmpl = output_path + '/' + formatted_title + '.wav'
         ydl_opts['outtmpl'] = formatted_outtmpl
         ydl = yt_dlp.YoutubeDL(ydl_opts)
         ydl.download([input_url])
        print(i18n("Audio downloaded!"))
    except Exception as error:
        print(i18n("An error occurred:"), error)

    actual_directory = os.path.dirname(__file__)
    
    vocal_directory = os.path.join(actual_directory, save_root_vocal)
    instrumental_directory = os.path.join(actual_directory, save_root_ins)
    
    vocal_formatted = f"vocal_{formatted_title}.wav.reformatted.wav_10.wav"
    instrumental_formatted = f"instrument_{formatted_title}.wav.reformatted.wav_10.wav"  
    
    vocal_audio_path = os.path.join(vocal_directory, vocal_formatted)
    instrumental_audio_path = os.path.join(instrumental_directory, instrumental_formatted)
    
    vocal_formatted_mdx = f"{formatted_title}_vocal_.wav"
    instrumental_formatted_mdx = f"{formatted_title}_instrument_.wav"
    
    vocal_audio_path_mdx = os.path.join(vocal_directory, vocal_formatted_mdx)
    instrumental_audio_path_mdx = os.path.join(instrumental_directory, instrumental_formatted_mdx)

    if architecture == "VR":
       try:
           print(i18n("Starting audio conversion... (This might take a moment)"))
           inp_root, save_root_vocal, save_root_ins = [x.strip(" ").strip('"').strip("\n").strip('"').strip(" ") for x in [inp_root, save_root_vocal, save_root_ins]]
           usable_files = [os.path.join(inp_root, file) 
                          for file in os.listdir(inp_root) 
                          if file.endswith(tuple(sup_audioext))]    
           
        
           pre_fun = MDXNetDereverb(15) if model_name == "onnx_dereverb_By_FoxJoy" else (_audio_pre_ if "DeEcho" not in model_name else _audio_pre_new)(
                       agg=int(agg),
                       model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
                       device=config.device,
                       is_half=config.is_half,
                   )
                
           try:
              if paths != None:
                paths = [path.name for path in paths]
              else:
                paths = usable_files
                
           except:
                traceback.print_exc()
                paths = usable_files
           print(paths) 
           for path in paths:
               inp_path = os.path.join(inp_root, path)
               need_reformat, done = 1, 0

               try:
                   info = ffmpeg.probe(inp_path, cmd="ffprobe")
                   if info["streams"][0]["channels"] == 2 and info["streams"][0]["sample_rate"] == "44100":
                       need_reformat = 0
                       pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal, format0)
                       done = 1
               except:
                   traceback.print_exc()

               if need_reformat:
                   tmp_path = f"{tmp}/{os.path.basename(inp_path)}.reformatted.wav"
                   os.system(f"ffmpeg -i {inp_path} -vn -acodec pcm_s16le -ac 2 -ar 44100 {tmp_path} -y")
                   inp_path = tmp_path

               try:
                   if not done:
                       pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal, format0)
                   print(f"{os.path.basename(inp_path)}->Success")
               except:
                   print(f"{os.path.basename(inp_path)}->{traceback.format_exc()}")
       except:
           traceback.print_exc()
       finally:
           try:
               if model_name == "onnx_dereverb_By_FoxJoy":
                   del pre_fun.pred.model
                   del pre_fun.pred.model_
               else:
                   del pre_fun.model

               del pre_fun
               return i18n("Finished"), vocal_audio_path, instrumental_audio_path
           except: traceback.print_exc()

           if torch.cuda.is_available(): torch.cuda.empty_cache()

    elif architecture == "MDX":
       try:
           print(i18n("Starting audio conversion... (This might take a moment)"))
           inp_root, save_root_vocal, save_root_ins = [x.strip(" ").strip('"').strip("\n").strip('"').strip(" ") for x in [inp_root, save_root_vocal, save_root_ins]]
        
           usable_files = [os.path.join(inp_root, file) 
                          for file in os.listdir(inp_root) 
                          if file.endswith(tuple(sup_audioext))]    
           try:
              if paths != None:
                paths = [path.name for path in paths]
              else:
                paths = usable_files
                
           except:
                traceback.print_exc()
                paths = usable_files
           print(paths) 
           invert=True
           denoise=True
           use_custom_parameter=True
           dim_f=2048
           dim_t=256
           n_fft=7680
           use_custom_compensation=True
           compensation=1.025
           suffix = "vocal_" #@param ["Vocals", "Drums", "Bass", "Other"]{allow-input: true}
           suffix_invert = "instrument_" #@param ["Instrumental", "Drumless", "Bassless", "Instruments"]{allow-input: true}
           print_settings = True  # @param{type:"boolean"}
           onnx = id_to_ptm(model_name)
           compensation = compensation if use_custom_compensation or use_custom_parameter else None
           mdx_model = prepare_mdx(onnx,use_custom_parameter, dim_f, dim_t, n_fft, compensation=compensation)
           
       
           for path in paths:
               #inp_path = os.path.join(inp_root, path)
               suffix_naming = suffix if use_custom_parameter else None
               diff_suffix_naming = suffix_invert if use_custom_parameter else None
               run_mdx(onnx, mdx_model, path, format0, diff=invert,suffix=suffix_naming,diff_suffix=diff_suffix_naming,denoise=denoise)
    
           if print_settings:
               print()
               print('[MDX-Net_Colab settings used]')
               print(f'Model used: {onnx}')
               print(f'Model MD5: {mdx.MDX.get_hash(onnx)}')
               print(f'Model parameters:')
               print(f'    -dim_f: {mdx_model.dim_f}')
               print(f'    -dim_t: {mdx_model.dim_t}')
               print(f'    -n_fft: {mdx_model.n_fft}')
               print(f'    -compensation: {mdx_model.compensation}')
               print()
               print('[Input file]')
               print('filename(s): ')
               for filename in paths:
                   print(f'    -{filename}')
                   print(f"{os.path.basename(filename)}->Success")
       except:
           traceback.print_exc()
       finally:
           try:
               del mdx_model
               return i18n("Finished"), vocal_audio_path_mdx, instrumental_audio_path_mdx
           except: traceback.print_exc()

           print("clean_empty_cache")

           if torch.cuda.is_available(): torch.cuda.empty_cache()
sup_audioext = {'wav', 'mp3', 'flac', 'ogg', 'opus',
                'm4a', 'mp4', 'aac', 'alac', 'wma',
                'aiff', 'webm', 'ac3'}

def load_downloaded_audio(url):
    parent_path = find_folder_parent(".", "pretrained_v2")
    try:
        infos = []
        audios_path = os.path.join(parent_path, 'audios')
        zips_path = os.path.join(parent_path, 'zips')

        if not os.path.exists(audios_path):
            os.mkdir(audios_path)
        
        download_file = download_from_url(url)
        if not download_file:
            print(i18n("The file could not be downloaded."))
            infos.append(i18n("The file could not be downloaded."))
            yield "\n".join(infos)
        elif download_file == "downloaded":
            print(i18n("It has been downloaded successfully."))
            infos.append(i18n("It has been downloaded successfully."))
            yield "\n".join(infos)
        elif download_file == "too much use":
            raise Exception(i18n("Too many users have recently viewed or downloaded this file"))
        elif download_file == "private link":
            raise Exception(i18n("Cannot get file from this private link"))
        
        for filename in os.listdir(zips_path):
            item_path = os.path.join(zips_path, filename)
            if item_path.split('.')[-1] in sup_audioext:
                if os.path.exists(item_path):
                    shutil.move(item_path, audios_path)
        
        result = ""
        print(i18n("Audio files have been moved to the 'audios' folder."))
        infos.append(i18n("Audio files have been moved to the 'audios' folder."))
        yield "\n".join(infos)
            
        os.chdir(parent_path)    
        return result
    except Exception as e:
        os.chdir(parent_path)
        if "too much use" in str(e):
            print(i18n("Too many users have recently viewed or downloaded this file"))
            yield i18n("Too many users have recently viewed or downloaded this file")
        elif "private link" in str(e):
            print(i18n("Cannot get file from this private link"))
            yield i18n("Cannot get file from this private link")
        else:
            print(e)
            yield i18n("An error occurred downloading")
    finally:
        os.chdir(parent_path)
 
       
class error_message(Exception):
    def __init__(self, mensaje):
        self.mensaje = mensaje
        super().__init__(mensaje)

def get_vc(sid, to_return_protect0, to_return_protect1):
    global n_spk, tgt_sr, net_g, vc, cpt, version
    if sid == "" or sid == []:
        global hubert_model
        if hubert_model is not None: 
            print("clean_empty_cache")
            del net_g, n_spk, vc, hubert_model, tgt_sr  
            hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
            if_f0 = cpt.get("f0", 1)
            version = cpt.get("version", "v1")
            if version == "v1":
                if if_f0 == 1:
                    net_g = SynthesizerTrnMs256NSFsid(
                        *cpt["config"], is_half=config.is_half
                    )
                else:
                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
            elif version == "v2":
                if if_f0 == 1:
                    net_g = SynthesizerTrnMs768NSFsid(
                        *cpt["config"], is_half=config.is_half
                    )
                else:
                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
            del net_g, cpt
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
            cpt = None
        return (
            {"visible": False, "__type__": "update"},
            {"visible": False, "__type__": "update"},
            {"visible": False, "__type__": "update"},
        )
    person = "%s/%s" % (weight_root, sid)
    print("loading %s" % person)
    cpt = torch.load(person, map_location="cpu")
    tgt_sr = cpt["config"][-1]
    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  
    if_f0 = cpt.get("f0", 1)
    if if_f0 == 0:
        to_return_protect0 = to_return_protect1 = {
            "visible": False,
            "value": 0.5,
            "__type__": "update",
        }
    else:
        to_return_protect0 = {
            "visible": True,
            "value": to_return_protect0,
            "__type__": "update",
        }
        to_return_protect1 = {
            "visible": True,
            "value": to_return_protect1,
            "__type__": "update",
        }
    version = cpt.get("version", "v1")
    if version == "v1":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
        else:
            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
    elif version == "v2":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
        else:
            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
    del net_g.enc_q
    print(net_g.load_state_dict(cpt["weight"], strict=False))
    net_g.eval().to(config.device)
    if config.is_half:
        net_g = net_g.half()
    else:
        net_g = net_g.float()
    vc = VC(tgt_sr, config)
    n_spk = cpt["config"][-3]
    return (
        {"visible": True, "maximum": n_spk, "__type__": "update"},
        to_return_protect0,
        to_return_protect1,
    )    
    
def update_model_choices(select_value):
    model_ids = get_model_list()
    model_ids_list = list(model_ids)
    if select_value == "VR":
        return {"choices": uvr5_names, "__type__": "update"}
    elif select_value == "MDX":
        return {"choices": model_ids_list, "__type__": "update"}

def download_model():
    gr.Markdown(value="# " + i18n("Download Model"))
    gr.Markdown(value=i18n("It is used to download your inference models."))
    with gr.Row():
        model_url=gr.Textbox(label=i18n("Url:"))
    with gr.Row():
        download_model_status_bar=gr.Textbox(label=i18n("Status:"))
    with gr.Row():
        download_button=gr.Button(i18n("Download"))
        download_button.click(fn=load_downloaded_model, inputs=[model_url], outputs=[download_model_status_bar])

def download_backup():
    gr.Markdown(value="# " + i18n("Download Backup"))
    gr.Markdown(value=i18n("It is used to download your training backups."))
    with gr.Row():
        model_url=gr.Textbox(label=i18n("Url:"))
    with gr.Row():
        download_model_status_bar=gr.Textbox(label=i18n("Status:"))
    with gr.Row():
        download_button=gr.Button(i18n("Download"))
        download_button.click(fn=load_downloaded_backup, inputs=[model_url], outputs=[download_model_status_bar])

def update_dataset_list(name):
    new_datasets = []
    for foldername in os.listdir("./datasets"):
        if "." not in foldername:
            new_datasets.append(os.path.join(find_folder_parent(".","pretrained"),"datasets",foldername))
    return gr.Dropdown.update(choices=new_datasets)

def download_dataset(trainset_dir4):
    gr.Markdown(value="# " + i18n("Download Dataset"))
    gr.Markdown(value=i18n("Download the dataset with the audios in a compatible format (.wav/.flac) to train your model."))
    with gr.Row():
        dataset_url=gr.Textbox(label=i18n("Url:"))
    with gr.Row():
        load_dataset_status_bar=gr.Textbox(label=i18n("Status:"))
    with gr.Row():
        load_dataset_button=gr.Button(i18n("Download"))
        load_dataset_button.click(fn=load_dowloaded_dataset, inputs=[dataset_url], outputs=[load_dataset_status_bar])
        load_dataset_status_bar.change(update_dataset_list, dataset_url, trainset_dir4)

def download_audio():
    gr.Markdown(value="# " + i18n("Download Audio"))
    gr.Markdown(value=i18n("Download audios of any format for use in inference (recommended for mobile users)."))
    with gr.Row():
        audio_url=gr.Textbox(label=i18n("Url:"))
    with gr.Row():
        download_audio_status_bar=gr.Textbox(label=i18n("Status:"))
    with gr.Row():
        download_button2=gr.Button(i18n("Download"))
        download_button2.click(fn=load_downloaded_audio, inputs=[audio_url], outputs=[download_audio_status_bar])

def youtube_separator():
        gr.Markdown(value="# " + i18n("Separate YouTube tracks"))
        gr.Markdown(value=i18n("Download audio from a YouTube video and automatically separate the vocal and instrumental tracks"))
        with gr.Row():
            input_url = gr.inputs.Textbox(label=i18n("Enter the YouTube link:"))
            output_path = gr.Textbox(
                label=i18n("Enter the path of the audio folder to be processed (copy it from the address bar of the file manager):"),
                value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/yt_downloads",
                visible=False,
                )
            advanced_settings_checkbox = gr.Checkbox(
                value=False,
                label=i18n("Advanced Settings"),
                interactive=True,
                )
        with gr.Row(label = i18n("Advanced Settings"), visible=False, variant='compact') as advanced_settings:
            with gr.Column(): 
                model_select = gr.Radio(
                    label=i18n("Model Architecture:"),
                    choices=["VR", "MDX"],
                    value="VR",
                    interactive=True,
                    )
                model_choose = gr.Dropdown(label=i18n("Model: (Be aware that in some models the named vocal will be the instrumental)"),                          
                    choices=uvr5_names,
                    value="HP5_only_main_vocal"   
                    )
                with gr.Row():
                    agg = gr.Slider(
                        minimum=0,
                        maximum=20,
                        step=1,
                        label=i18n("Vocal Extraction Aggressive"),
                        value=10,
                        interactive=True,
                        )
                with gr.Row():            
                    opt_vocal_root = gr.Textbox(
                        label=i18n("Specify the output folder for vocals:"), value="audios",
                        )
                opt_ins_root = gr.Textbox(
                    label=i18n("Specify the output folder for accompaniment:"), value="audio-others",
                    ) 
                dir_wav_input = gr.Textbox(
                    label=i18n("Enter the path of the audio folder to be processed:"),
                    value=((os.getcwd()).replace('\\', '/') + "/yt_downloads"),
                    visible=False,
                    )
                format0 = gr.Radio(
                    label=i18n("Export file format"),
                    choices=["wav", "flac", "mp3", "m4a"],
                    value="wav",
                    visible=False,
                    interactive=True,
                    )
                wav_inputs = gr.File(
                    file_count="multiple", label=i18n("You can also input audio files in batches. Choose one of the two options. Priority is given to reading from the folder."),
                    visible=False,
                    )
            model_select.change(
                fn=update_model_choices,
                inputs=model_select,
                outputs=model_choose,
                )
        with gr.Row():
            vc_output4 = gr.Textbox(label=i18n("Status:"))
            vc_output5 = gr.Audio(label=i18n("Vocal"), type='filepath')
            vc_output6 = gr.Audio(label=i18n("Instrumental"), type='filepath')
        with gr.Row():
            but2 = gr.Button(i18n("Download and Separate"))
            but2.click(
                uvr,
                    [
                    input_url, 
                    output_path,
                    model_choose,
                    dir_wav_input,
                    opt_vocal_root,
                    wav_inputs,
                    opt_ins_root,
                    agg,
                    format0,
                    model_select
                    ],
                    [vc_output4, vc_output5, vc_output6],
                )
        def toggle_advanced_settings(checkbox):
            return {"visible": checkbox, "__type__": "update"}
        
        advanced_settings_checkbox.change(
            fn=toggle_advanced_settings,
            inputs=[advanced_settings_checkbox],
            outputs=[advanced_settings]
            )


def get_bark_voice():
    mensaje = """
v2/en_speaker_0	English	Male
v2/en_speaker_1	English	Male
v2/en_speaker_2	English	Male
v2/en_speaker_3	English	Male
v2/en_speaker_4	English	Male
v2/en_speaker_5	English	Male
v2/en_speaker_6	English	Male
v2/en_speaker_7	English	Male
v2/en_speaker_8	English	Male
v2/en_speaker_9	English	Female
v2/zh_speaker_0	Chinese (Simplified)	Male
v2/zh_speaker_1	Chinese (Simplified)	Male
v2/zh_speaker_2	Chinese (Simplified)	Male
v2/zh_speaker_3	Chinese (Simplified)	Male
v2/zh_speaker_4	Chinese (Simplified)	Female
v2/zh_speaker_5	Chinese (Simplified)	Male
v2/zh_speaker_6	Chinese (Simplified)	Female
v2/zh_speaker_7	Chinese (Simplified)	Female
v2/zh_speaker_8	Chinese (Simplified)	Male
v2/zh_speaker_9	Chinese (Simplified)	Female
v2/fr_speaker_0	French	Male
v2/fr_speaker_1	French	Female
v2/fr_speaker_2	French	Female
v2/fr_speaker_3	French	Male
v2/fr_speaker_4	French	Male
v2/fr_speaker_5	French	Female
v2/fr_speaker_6	French	Male
v2/fr_speaker_7	French	Male
v2/fr_speaker_8	French	Male
v2/fr_speaker_9	French	Male
v2/de_speaker_0	German	Male
v2/de_speaker_1	German	Male
v2/de_speaker_2	German	Male
v2/de_speaker_3	German	Female
v2/de_speaker_4	German	Male
v2/de_speaker_5	German	Male
v2/de_speaker_6	German	Male
v2/de_speaker_7	German	Male
v2/de_speaker_8	German	Female
v2/de_speaker_9	German	Male
v2/hi_speaker_0	Hindi	Female
v2/hi_speaker_1	Hindi	Female
v2/hi_speaker_2	Hindi	Male
v2/hi_speaker_3	Hindi	Female
v2/hi_speaker_4	Hindi	Female
v2/hi_speaker_5	Hindi	Male
v2/hi_speaker_6	Hindi	Male
v2/hi_speaker_7	Hindi	Male
v2/hi_speaker_8	Hindi	Male
v2/hi_speaker_9	Hindi	Female
v2/it_speaker_0	Italian	Male
v2/it_speaker_1	Italian	Male
v2/it_speaker_2	Italian	Female
v2/it_speaker_3	Italian	Male
v2/it_speaker_4	Italian	Male
v2/it_speaker_5	Italian	Male
v2/it_speaker_6	Italian	Male
v2/it_speaker_7	Italian	Female
v2/it_speaker_8	Italian	Male
v2/it_speaker_9	Italian	Female
v2/ja_speaker_0	Japanese	Female
v2/ja_speaker_1	Japanese	Female
v2/ja_speaker_2	Japanese	Male
v2/ja_speaker_3	Japanese	Female
v2/ja_speaker_4	Japanese	Female
v2/ja_speaker_5	Japanese	Female
v2/ja_speaker_6	Japanese	Male
v2/ja_speaker_7	Japanese	Female
v2/ja_speaker_8	Japanese	Female
v2/ja_speaker_9	Japanese	Female
v2/ko_speaker_0	Korean	Female
v2/ko_speaker_1	Korean	Male
v2/ko_speaker_2	Korean	Male
v2/ko_speaker_3	Korean	Male
v2/ko_speaker_4	Korean	Male
v2/ko_speaker_5	Korean	Male
v2/ko_speaker_6	Korean	Male
v2/ko_speaker_7	Korean	Male
v2/ko_speaker_8	Korean	Male
v2/ko_speaker_9	Korean	Male
v2/pl_speaker_0	Polish	Male
v2/pl_speaker_1	Polish	Male
v2/pl_speaker_2	Polish	Male
v2/pl_speaker_3	Polish	Male
v2/pl_speaker_4	Polish	Female
v2/pl_speaker_5	Polish	Male
v2/pl_speaker_6	Polish	Female
v2/pl_speaker_7	Polish	Male
v2/pl_speaker_8	Polish	Male
v2/pl_speaker_9	Polish	Female
v2/pt_speaker_0	Portuguese	Male
v2/pt_speaker_1	Portuguese	Male
v2/pt_speaker_2	Portuguese	Male
v2/pt_speaker_3	Portuguese	Male
v2/pt_speaker_4	Portuguese	Male
v2/pt_speaker_5	Portuguese	Male
v2/pt_speaker_6	Portuguese	Male
v2/pt_speaker_7	Portuguese	Male
v2/pt_speaker_8	Portuguese	Male
v2/pt_speaker_9	Portuguese	Male
v2/ru_speaker_0	Russian	Male
v2/ru_speaker_1	Russian	Male
v2/ru_speaker_2	Russian	Male
v2/ru_speaker_3	Russian	Male
v2/ru_speaker_4	Russian	Male
v2/ru_speaker_5	Russian	Female
v2/ru_speaker_6	Russian	Female
v2/ru_speaker_7	Russian	Male
v2/ru_speaker_8	Russian	Male
v2/ru_speaker_9	Russian	Female
v2/es_speaker_0	Spanish	Male
v2/es_speaker_1	Spanish	Male
v2/es_speaker_2	Spanish	Male
v2/es_speaker_3	Spanish	Male
v2/es_speaker_4	Spanish	Male
v2/es_speaker_5	Spanish	Male
v2/es_speaker_6	Spanish	Male
v2/es_speaker_7	Spanish	Male
v2/es_speaker_8	Spanish	Female
v2/es_speaker_9	Spanish	Female
v2/tr_speaker_0	Turkish	Male
v2/tr_speaker_1	Turkish	Male
v2/tr_speaker_2	Turkish	Male
v2/tr_speaker_3	Turkish	Male
v2/tr_speaker_4	Turkish	Female
v2/tr_speaker_5	Turkish	Female
v2/tr_speaker_6	Turkish	Male
v2/tr_speaker_7	Turkish	Male
v2/tr_speaker_8	Turkish	Male
v2/tr_speaker_9	Turkish	Male
    """
# Dividir el mensaje en líneas
    lineas = mensaje.split("\n")
    datos_deseados = []
    for linea in lineas:
        partes = linea.split("\t")
        if len(partes) == 3:
            clave, _, genero = partes  
            datos_deseados.append(f"{clave}-{genero}")

    return datos_deseados

    
def get_edge_voice():
    completed_process = subprocess.run(['edge-tts',"-l"], capture_output=True, text=True)
    lines = completed_process.stdout.strip().split("\n")
    data = []
    current_entry = {}
    for line in lines:
        if line.startswith("Name: "):
            if current_entry:
                data.append(current_entry)
            current_entry = {"Name": line.split(": ")[1]}
        elif line.startswith("Gender: "):
            current_entry["Gender"] = line.split(": ")[1]
    if current_entry:
        data.append(current_entry)
    tts_voice = []
    for entry in data:
        name = entry["Name"]
        gender = entry["Gender"]
        formatted_entry = f'{name}-{gender}'
        tts_voice.append(formatted_entry)
    return tts_voice


#print(set_tts_voice)