Spaces:
Runtime error
Runtime error
File size: 54,451 Bytes
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 |
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 download_from_url(url):
parent_path = find_folder_parent(".", "pretrained_v2")
zips_path = os.path.join(parent_path, 'zips')
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(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)
|