File size: 51,380 Bytes
b152010 |
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 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 |
from gtts import gTTS
import edge_tts, asyncio, json, glob # noqa
from tqdm import tqdm
import librosa, os, re, torch, gc, subprocess # noqa
from .language_configuration import (
fix_code_language,
BARK_VOICES_LIST,
VITS_VOICES_LIST,
)
from .utils import (
download_manager,
create_directories,
copy_files,
rename_file,
remove_directory_contents,
remove_files,
run_command,
)
import numpy as np
from typing import Any, Dict
from pathlib import Path
import soundfile as sf
import platform
import logging
import traceback
from .logging_setup import logger
class TTS_OperationError(Exception):
def __init__(self, message="The operation did not complete successfully."):
self.message = message
super().__init__(self.message)
def verify_saved_file_and_size(filename):
if not os.path.exists(filename):
raise TTS_OperationError(f"File '{filename}' was not saved.")
if os.path.getsize(filename) == 0:
raise TTS_OperationError(
f"File '{filename}' has a zero size. "
"Related to incorrect TTS for the target language"
)
def error_handling_in_tts(error, segment, TRANSLATE_AUDIO_TO, filename):
traceback.print_exc()
logger.error(f"Error: {str(error)}")
try:
from tempfile import TemporaryFile
tts = gTTS(segment["text"], lang=fix_code_language(TRANSLATE_AUDIO_TO))
# tts.save(filename)
f = TemporaryFile()
tts.write_to_fp(f)
# Reset the file pointer to the beginning of the file
f.seek(0)
# Read audio data from the TemporaryFile using soundfile
audio_data, samplerate = sf.read(f)
f.close() # Close the TemporaryFile
sf.write(
filename, audio_data, samplerate, format="ogg", subtype="vorbis"
)
logger.warning(
'TTS auxiliary will be utilized '
f'rather than TTS: {segment["tts_name"]}'
)
verify_saved_file_and_size(filename)
except Exception as error:
logger.critical(f"Error: {str(error)}")
sample_rate_aux = 22050
duration = float(segment["end"]) - float(segment["start"])
data = np.zeros(int(sample_rate_aux * duration)).astype(np.float32)
sf.write(
filename, data, sample_rate_aux, format="ogg", subtype="vorbis"
)
logger.error("Audio will be replaced -> [silent audio].")
verify_saved_file_and_size(filename)
def pad_array(array, sr):
if isinstance(array, list):
array = np.array(array)
if not array.shape[0]:
raise ValueError("The generated audio does not contain any data")
valid_indices = np.where(np.abs(array) > 0.001)[0]
if len(valid_indices) == 0:
logger.debug(f"No valid indices: {array}")
return array
try:
pad_indice = int(0.1 * sr)
start_pad = max(0, valid_indices[0] - pad_indice)
end_pad = min(len(array), valid_indices[-1] + 1 + pad_indice)
padded_array = array[start_pad:end_pad]
return padded_array
except Exception as error:
logger.error(str(error))
return array
# =====================================
# EDGE TTS
# =====================================
def edge_tts_voices_list():
try:
completed_process = subprocess.run(
["edge-tts", "--list-voices"], capture_output=True, text=True
)
lines = completed_process.stdout.strip().split("\n")
except Exception as error:
logger.debug(str(error))
lines = []
voices = []
for line in lines:
if line.startswith("Name: "):
voice_entry = {}
voice_entry["Name"] = line.split(": ")[1]
elif line.startswith("Gender: "):
voice_entry["Gender"] = line.split(": ")[1]
voices.append(voice_entry)
formatted_voices = [
f"{entry['Name']}-{entry['Gender']}" for entry in voices
]
if not formatted_voices:
logger.warning(
"The list of Edge TTS voices could not be obtained, "
"switching to an alternative method"
)
tts_voice_list = asyncio.new_event_loop().run_until_complete(
edge_tts.list_voices()
)
formatted_voices = sorted(
[f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
)
if not formatted_voices:
logger.error("Can't get EDGE TTS - list voices")
return formatted_voices
def segments_egde_tts(filtered_edge_segments, TRANSLATE_AUDIO_TO, is_gui):
for segment in tqdm(filtered_edge_segments["segments"]):
speaker = segment["speaker"] # noqa
text = segment["text"]
start = segment["start"]
tts_name = segment["tts_name"]
# make the tts audio
filename = f"audio/{start}.ogg"
temp_file = filename[:-3] + "mp3"
logger.info(f"{text} >> {filename}")
try:
if is_gui:
asyncio.run(
edge_tts.Communicate(
text, "-".join(tts_name.split("-")[:-1])
).save(temp_file)
)
else:
# nest_asyncio.apply() if not is_gui else None
command = f'edge-tts -t "{text}" -v "{tts_name.replace("-Male", "").replace("-Female", "")}" --write-media "{temp_file}"'
run_command(command)
verify_saved_file_and_size(temp_file)
data, sample_rate = sf.read(temp_file)
data = pad_array(data, sample_rate)
# os.remove(temp_file)
# Save file
sf.write(
file=filename,
samplerate=sample_rate,
data=data,
format="ogg",
subtype="vorbis",
)
verify_saved_file_and_size(filename)
except Exception as error:
error_handling_in_tts(error, segment, TRANSLATE_AUDIO_TO, filename)
# =====================================
# BARK TTS
# =====================================
def segments_bark_tts(
filtered_bark_segments, TRANSLATE_AUDIO_TO, model_id_bark="suno/bark-small"
):
from transformers import AutoProcessor, BarkModel
from optimum.bettertransformer import BetterTransformer
device = os.environ.get("SONITR_DEVICE")
torch_dtype_env = torch.float16 if device == "cuda" else torch.float32
# load model bark
model = BarkModel.from_pretrained(
model_id_bark, torch_dtype=torch_dtype_env
).to(device)
model = model.to(device)
processor = AutoProcessor.from_pretrained(
model_id_bark, return_tensors="pt"
) # , padding=True
if device == "cuda":
# convert to bettertransformer
model = BetterTransformer.transform(model, keep_original_model=False)
# enable CPU offload
# model.enable_cpu_offload()
sampling_rate = model.generation_config.sample_rate
# filtered_segments = filtered_bark_segments['segments']
# Sorting the segments by 'tts_name'
# sorted_segments = sorted(filtered_segments, key=lambda x: x['tts_name'])
# logger.debug(sorted_segments)
for segment in tqdm(filtered_bark_segments["segments"]):
speaker = segment["speaker"] # noqa
text = segment["text"]
start = segment["start"]
tts_name = segment["tts_name"]
inputs = processor(text, voice_preset=BARK_VOICES_LIST[tts_name]).to(
device
)
# make the tts audio
filename = f"audio/{start}.ogg"
logger.info(f"{text} >> {filename}")
try:
# Infer
with torch.inference_mode():
speech_output = model.generate(
**inputs,
do_sample=True,
fine_temperature=0.4,
coarse_temperature=0.8,
pad_token_id=processor.tokenizer.pad_token_id,
)
# Save file
data_tts = pad_array(
speech_output.cpu().numpy().squeeze().astype(np.float32),
sampling_rate,
)
sf.write(
file=filename,
samplerate=sampling_rate,
data=data_tts,
format="ogg",
subtype="vorbis",
)
verify_saved_file_and_size(filename)
except Exception as error:
error_handling_in_tts(error, segment, TRANSLATE_AUDIO_TO, filename)
gc.collect()
torch.cuda.empty_cache()
try:
del processor
del model
gc.collect()
torch.cuda.empty_cache()
except Exception as error:
logger.error(str(error))
gc.collect()
torch.cuda.empty_cache()
# =====================================
# VITS TTS
# =====================================
def uromanize(input_string):
"""Convert non-Roman strings to Roman using the `uroman` perl package."""
# script_path = os.path.join(uroman_path, "bin", "uroman.pl")
if not os.path.exists("./uroman"):
logger.info(
"Clonning repository uroman https://github.com/isi-nlp/uroman.git"
" for romanize the text"
)
process = subprocess.Popen(
["git", "clone", "https://github.com/isi-nlp/uroman.git"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
stdout, stderr = process.communicate()
script_path = os.path.join("./uroman", "bin", "uroman.pl")
command = ["perl", script_path]
process = subprocess.Popen(
command,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
# Execute the perl command
stdout, stderr = process.communicate(input=input_string.encode())
if process.returncode != 0:
raise ValueError(f"Error {process.returncode}: {stderr.decode()}")
# Return the output as a string and skip the new-line character at the end
return stdout.decode()[:-1]
def segments_vits_tts(filtered_vits_segments, TRANSLATE_AUDIO_TO):
from transformers import VitsModel, AutoTokenizer
filtered_segments = filtered_vits_segments["segments"]
# Sorting the segments by 'tts_name'
sorted_segments = sorted(filtered_segments, key=lambda x: x["tts_name"])
logger.debug(sorted_segments)
model_name_key = None
for segment in tqdm(sorted_segments):
speaker = segment["speaker"] # noqa
text = segment["text"]
start = segment["start"]
tts_name = segment["tts_name"]
if tts_name != model_name_key:
model_name_key = tts_name
model = VitsModel.from_pretrained(VITS_VOICES_LIST[tts_name])
tokenizer = AutoTokenizer.from_pretrained(
VITS_VOICES_LIST[tts_name]
)
sampling_rate = model.config.sampling_rate
if tokenizer.is_uroman:
romanize_text = uromanize(text)
logger.debug(f"Romanize text: {romanize_text}")
inputs = tokenizer(romanize_text, return_tensors="pt")
else:
inputs = tokenizer(text, return_tensors="pt")
# make the tts audio
filename = f"audio/{start}.ogg"
logger.info(f"{text} >> {filename}")
try:
# Infer
with torch.no_grad():
speech_output = model(**inputs).waveform
data_tts = pad_array(
speech_output.cpu().numpy().squeeze().astype(np.float32),
sampling_rate,
)
# Save file
sf.write(
file=filename,
samplerate=sampling_rate,
data=data_tts,
format="ogg",
subtype="vorbis",
)
verify_saved_file_and_size(filename)
except Exception as error:
error_handling_in_tts(error, segment, TRANSLATE_AUDIO_TO, filename)
gc.collect()
torch.cuda.empty_cache()
try:
del tokenizer
del model
gc.collect()
torch.cuda.empty_cache()
except Exception as error:
logger.error(str(error))
gc.collect()
torch.cuda.empty_cache()
# =====================================
# Coqui XTTS
# =====================================
def coqui_xtts_voices_list():
main_folder = "_XTTS_"
pattern_coqui = re.compile(r".+\.(wav|mp3|ogg|m4a)$")
pattern_automatic_speaker = re.compile(r"AUTOMATIC_SPEAKER_\d+\.wav$")
# List only files in the directory matching the pattern but not matching
# AUTOMATIC_SPEAKER_00.wav, AUTOMATIC_SPEAKER_01.wav, etc.
wav_voices = [
"_XTTS_/" + f
for f in os.listdir(main_folder)
if os.path.isfile(os.path.join(main_folder, f))
and pattern_coqui.match(f)
and not pattern_automatic_speaker.match(f)
]
return ["_XTTS_/AUTOMATIC.wav"] + wav_voices
def seconds_to_hhmmss_ms(seconds):
hours = seconds // 3600
minutes = (seconds % 3600) // 60
seconds = seconds % 60
milliseconds = int((seconds - int(seconds)) * 1000)
return "%02d:%02d:%02d.%03d" % (hours, minutes, int(seconds), milliseconds)
def audio_trimming(audio_path, destination, start, end):
if isinstance(start, (int, float)):
start = seconds_to_hhmmss_ms(start)
if isinstance(end, (int, float)):
end = seconds_to_hhmmss_ms(end)
if destination:
file_directory = destination
else:
file_directory = os.path.dirname(audio_path)
file_name = os.path.splitext(os.path.basename(audio_path))[0]
file_ = f"{file_name}_trim.wav"
# file_ = f'{os.path.splitext(audio_path)[0]}_trim.wav'
output_path = os.path.join(file_directory, file_)
# -t (duration from -ss) | -to (time stop) | -af silenceremove=1:0:-50dB (remove silence)
command = f'ffmpeg -y -loglevel error -i "{audio_path}" -ss {start} -to {end} -acodec pcm_s16le -f wav "{output_path}"'
run_command(command)
return output_path
def convert_to_xtts_good_sample(audio_path: str = "", destination: str = ""):
if destination:
file_directory = destination
else:
file_directory = os.path.dirname(audio_path)
file_name = os.path.splitext(os.path.basename(audio_path))[0]
file_ = f"{file_name}_good_sample.wav"
# file_ = f'{os.path.splitext(audio_path)[0]}_good_sample.wav'
mono_path = os.path.join(file_directory, file_) # get root
command = f'ffmpeg -y -loglevel error -i "{audio_path}" -ac 1 -ar 22050 -sample_fmt s16 -f wav "{mono_path}"'
run_command(command)
return mono_path
def sanitize_file_name(file_name):
import unicodedata
# Normalize the string to NFKD form to separate combined characters into
# base characters and diacritics
normalized_name = unicodedata.normalize("NFKD", file_name)
# Replace any non-ASCII characters or special symbols with an underscore
sanitized_name = re.sub(r"[^\w\s.-]", "_", normalized_name)
return sanitized_name
def create_wav_file_vc(
sample_name="", # name final file
audio_wav="", # path
start=None, # trim start
end=None, # trim end
output_final_path="_XTTS_",
get_vocals_dereverb=True,
):
sample_name = sample_name if sample_name else "default_name"
sample_name = sanitize_file_name(sample_name)
audio_wav = audio_wav if isinstance(audio_wav, str) else audio_wav.name
BASE_DIR = (
"." # os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
)
output_dir = os.path.join(BASE_DIR, "clean_song_output") # remove content
# remove_directory_contents(output_dir)
if start or end:
# Cut file
audio_segment = audio_trimming(audio_wav, output_dir, start, end)
else:
# Complete file
audio_segment = audio_wav
from .mdx_net import process_uvr_task
try:
_, _, _, _, audio_segment = process_uvr_task(
orig_song_path=audio_segment,
main_vocals=True,
dereverb=get_vocals_dereverb,
)
except Exception as error:
logger.error(str(error))
sample = convert_to_xtts_good_sample(audio_segment)
sample_name = f"{sample_name}.wav"
sample_rename = rename_file(sample, sample_name)
copy_files(sample_rename, output_final_path)
final_sample = os.path.join(output_final_path, sample_name)
if os.path.exists(final_sample):
logger.info(final_sample)
return final_sample
else:
raise Exception(f"Error wav: {final_sample}")
def create_new_files_for_vc(
speakers_coqui,
segments_base,
dereverb_automatic=True
):
# before function delete automatic delete_previous_automatic
output_dir = os.path.join(".", "clean_song_output") # remove content
remove_directory_contents(output_dir)
for speaker in speakers_coqui:
filtered_speaker = [
segment
for segment in segments_base
if segment["speaker"] == speaker
]
if len(filtered_speaker) > 4:
filtered_speaker = filtered_speaker[1:]
if filtered_speaker[0]["tts_name"] == "_XTTS_/AUTOMATIC.wav":
name_automatic_wav = f"AUTOMATIC_{speaker}"
if os.path.exists(f"_XTTS_/{name_automatic_wav}.wav"):
logger.info(f"WAV automatic {speaker} exists")
# path_wav = path_automatic_wav
pass
else:
# create wav
wav_ok = False
for seg in filtered_speaker:
duration = float(seg["end"]) - float(seg["start"])
if duration > 7.0 and duration < 12.0:
logger.info(
f'Processing segment: {seg["start"]}, {seg["end"]}, {seg["speaker"]}, {duration}, {seg["text"]}'
)
create_wav_file_vc(
sample_name=name_automatic_wav,
audio_wav="audio.wav",
start=(float(seg["start"]) + 1.0),
end=(float(seg["end"]) - 1.0),
get_vocals_dereverb=dereverb_automatic,
)
wav_ok = True
break
if not wav_ok:
logger.info("Taking the first segment")
seg = filtered_speaker[0]
logger.info(
f'Processing segment: {seg["start"]}, {seg["end"]}, {seg["speaker"]}, {seg["text"]}'
)
max_duration = float(seg["end"]) - float(seg["start"])
max_duration = max(2.0, min(max_duration, 9.0))
create_wav_file_vc(
sample_name=name_automatic_wav,
audio_wav="audio.wav",
start=(float(seg["start"])),
end=(float(seg["start"]) + max_duration),
get_vocals_dereverb=dereverb_automatic,
)
def segments_coqui_tts(
filtered_coqui_segments,
TRANSLATE_AUDIO_TO,
model_id_coqui="tts_models/multilingual/multi-dataset/xtts_v2",
speakers_coqui=None,
delete_previous_automatic=True,
dereverb_automatic=True,
emotion=None,
):
"""XTTS
Install:
pip install -q TTS==0.21.1
pip install -q numpy==1.23.5
Notes:
- tts_name is the wav|mp3|ogg|m4a file for VC
"""
from TTS.api import TTS
TRANSLATE_AUDIO_TO = fix_code_language(TRANSLATE_AUDIO_TO, syntax="coqui")
supported_lang_coqui = [
"zh-cn",
"en",
"fr",
"de",
"it",
"pt",
"pl",
"tr",
"ru",
"nl",
"cs",
"ar",
"es",
"hu",
"ko",
"ja",
]
if TRANSLATE_AUDIO_TO not in supported_lang_coqui:
raise TTS_OperationError(
f"'{TRANSLATE_AUDIO_TO}' is not a supported language for Coqui XTTS"
)
# Emotion and speed can only be used with Coqui Studio models. discontinued
# emotions = ["Neutral", "Happy", "Sad", "Angry", "Dull"]
if delete_previous_automatic:
for spk in speakers_coqui:
remove_files(f"_XTTS_/AUTOMATIC_{spk}.wav")
directory_audios_vc = "_XTTS_"
create_directories(directory_audios_vc)
create_new_files_for_vc(
speakers_coqui,
filtered_coqui_segments["segments"],
dereverb_automatic,
)
# Init TTS
device = os.environ.get("SONITR_DEVICE")
model = TTS(model_id_coqui).to(device)
sampling_rate = 24000
# filtered_segments = filtered_coqui_segments['segments']
# Sorting the segments by 'tts_name'
# sorted_segments = sorted(filtered_segments, key=lambda x: x['tts_name'])
# logger.debug(sorted_segments)
for segment in tqdm(filtered_coqui_segments["segments"]):
speaker = segment["speaker"]
text = segment["text"]
start = segment["start"]
tts_name = segment["tts_name"]
if tts_name == "_XTTS_/AUTOMATIC.wav":
tts_name = f"_XTTS_/AUTOMATIC_{speaker}.wav"
# make the tts audio
filename = f"audio/{start}.ogg"
logger.info(f"{text} >> {filename}")
try:
# Infer
wav = model.tts(
text=text, speaker_wav=tts_name, language=TRANSLATE_AUDIO_TO
)
data_tts = pad_array(
wav,
sampling_rate,
)
# Save file
sf.write(
file=filename,
samplerate=sampling_rate,
data=data_tts,
format="ogg",
subtype="vorbis",
)
verify_saved_file_and_size(filename)
except Exception as error:
error_handling_in_tts(error, segment, TRANSLATE_AUDIO_TO, filename)
gc.collect()
torch.cuda.empty_cache()
try:
del model
gc.collect()
torch.cuda.empty_cache()
except Exception as error:
logger.error(str(error))
gc.collect()
torch.cuda.empty_cache()
# =====================================
# PIPER TTS
# =====================================
def piper_tts_voices_list():
file_path = download_manager(
url="https://huggingface.co./rhasspy/piper-voices/resolve/main/voices.json",
path="./PIPER_MODELS",
)
with open(file_path, "r", encoding="utf8") as file:
data = json.load(file)
piper_id_models = [key + " VITS-onnx" for key in data.keys()]
return piper_id_models
def replace_text_in_json(file_path, key_to_replace, new_text, condition=None):
# Read the JSON file
with open(file_path, "r", encoding="utf-8") as file:
data = json.load(file)
# Modify the specified key's value with the new text
if key_to_replace in data:
if condition:
value_condition = condition
else:
value_condition = data[key_to_replace]
if data[key_to_replace] == value_condition:
data[key_to_replace] = new_text
# Write the modified content back to the JSON file
with open(file_path, "w") as file:
json.dump(
data, file, indent=2
) # Write the modified data back to the file with indentation for readability
def load_piper_model(
model: str,
data_dir: list,
download_dir: str = "",
update_voices: bool = False,
):
from piper import PiperVoice
from piper.download import ensure_voice_exists, find_voice, get_voices
try:
import onnxruntime as rt
if rt.get_device() == "GPU" and os.environ.get("SONITR_DEVICE") == "cuda":
logger.debug("onnxruntime device > GPU")
cuda = True
else:
logger.info(
"onnxruntime device > CPU"
) # try pip install onnxruntime-gpu
cuda = False
except Exception as error:
raise TTS_OperationError(f"onnxruntime error: {str(error)}")
# Disable CUDA in Windows
if platform.system() == "Windows":
logger.info("Employing CPU exclusivity with Piper TTS")
cuda = False
if not download_dir:
# Download to first data directory by default
download_dir = data_dir[0]
else:
data_dir = [os.path.join(data_dir[0], download_dir)]
# Download voice if file doesn't exist
model_path = Path(model)
if not model_path.exists():
# Load voice info
voices_info = get_voices(download_dir, update_voices=update_voices)
# Resolve aliases for backwards compatibility with old voice names
aliases_info: Dict[str, Any] = {}
for voice_info in voices_info.values():
for voice_alias in voice_info.get("aliases", []):
aliases_info[voice_alias] = {"_is_alias": True, **voice_info}
voices_info.update(aliases_info)
ensure_voice_exists(model, data_dir, download_dir, voices_info)
model, config = find_voice(model, data_dir)
replace_text_in_json(
config, "phoneme_type", "espeak", "PhonemeType.ESPEAK"
)
# Load voice
voice = PiperVoice.load(model, config_path=config, use_cuda=cuda)
return voice
def synthesize_text_to_audio_np_array(voice, text, synthesize_args):
audio_stream = voice.synthesize_stream_raw(text, **synthesize_args)
# Collect the audio bytes into a single NumPy array
audio_data = b""
for audio_bytes in audio_stream:
audio_data += audio_bytes
# Ensure correct data type and convert audio bytes to NumPy array
audio_np = np.frombuffer(audio_data, dtype=np.int16)
return audio_np
def segments_vits_onnx_tts(filtered_onnx_vits_segments, TRANSLATE_AUDIO_TO):
"""
Install:
pip install -q piper-tts==1.2.0 onnxruntime-gpu # for cuda118
"""
data_dir = [
str(Path.cwd())
] # "Data directory to check for downloaded models (default: current directory)"
download_dir = "PIPER_MODELS"
# model_name = "en_US-lessac-medium" tts_name in a dict like VITS
update_voices = True # "Download latest voices.json during startup",
synthesize_args = {
"speaker_id": None,
"length_scale": 1.0,
"noise_scale": 0.667,
"noise_w": 0.8,
"sentence_silence": 0.0,
}
filtered_segments = filtered_onnx_vits_segments["segments"]
# Sorting the segments by 'tts_name'
sorted_segments = sorted(filtered_segments, key=lambda x: x["tts_name"])
logger.debug(sorted_segments)
model_name_key = None
for segment in tqdm(sorted_segments):
speaker = segment["speaker"] # noqa
text = segment["text"]
start = segment["start"]
tts_name = segment["tts_name"].replace(" VITS-onnx", "")
if tts_name != model_name_key:
model_name_key = tts_name
model = load_piper_model(
tts_name, data_dir, download_dir, update_voices
)
sampling_rate = model.config.sample_rate
# make the tts audio
filename = f"audio/{start}.ogg"
logger.info(f"{text} >> {filename}")
try:
# Infer
speech_output = synthesize_text_to_audio_np_array(
model, text, synthesize_args
)
data_tts = pad_array(
speech_output, # .cpu().numpy().squeeze().astype(np.float32),
sampling_rate,
)
# Save file
sf.write(
file=filename,
samplerate=sampling_rate,
data=data_tts,
format="ogg",
subtype="vorbis",
)
verify_saved_file_and_size(filename)
except Exception as error:
error_handling_in_tts(error, segment, TRANSLATE_AUDIO_TO, filename)
gc.collect()
torch.cuda.empty_cache()
try:
del model
gc.collect()
torch.cuda.empty_cache()
except Exception as error:
logger.error(str(error))
gc.collect()
torch.cuda.empty_cache()
# =====================================
# CLOSEAI TTS
# =====================================
def segments_openai_tts(
filtered_openai_tts_segments, TRANSLATE_AUDIO_TO
):
from openai import OpenAI
client = OpenAI()
sampling_rate = 24000
# filtered_segments = filtered_openai_tts_segments['segments']
# Sorting the segments by 'tts_name'
# sorted_segments = sorted(filtered_segments, key=lambda x: x['tts_name'])
for segment in tqdm(filtered_openai_tts_segments["segments"]):
speaker = segment["speaker"] # noqa
text = segment["text"].strip()
start = segment["start"]
tts_name = segment["tts_name"]
# make the tts audio
filename = f"audio/{start}.ogg"
logger.info(f"{text} >> {filename}")
try:
# Request
response = client.audio.speech.create(
model="tts-1-hd" if "HD" in tts_name else "tts-1",
voice=tts_name.split()[0][1:],
response_format="wav",
input=text
)
audio_bytes = b''
for data in response.iter_bytes(chunk_size=4096):
audio_bytes += data
speech_output = np.frombuffer(audio_bytes, dtype=np.int16)
# Save file
data_tts = pad_array(
speech_output[240:],
sampling_rate,
)
sf.write(
file=filename,
samplerate=sampling_rate,
data=data_tts,
format="ogg",
subtype="vorbis",
)
verify_saved_file_and_size(filename)
except Exception as error:
error_handling_in_tts(error, segment, TRANSLATE_AUDIO_TO, filename)
# =====================================
# Select task TTS
# =====================================
def find_spkr(pattern, speaker_to_voice, segments):
return [
speaker
for speaker, voice in speaker_to_voice.items()
if pattern.match(voice) and any(
segment["speaker"] == speaker for segment in segments
)
]
def filter_by_speaker(speakers, segments):
return {
"segments": [
segment
for segment in segments
if segment["speaker"] in speakers
]
}
def audio_segmentation_to_voice(
result_diarize,
TRANSLATE_AUDIO_TO,
is_gui,
tts_voice00,
tts_voice01="",
tts_voice02="",
tts_voice03="",
tts_voice04="",
tts_voice05="",
tts_voice06="",
tts_voice07="",
tts_voice08="",
tts_voice09="",
tts_voice10="",
tts_voice11="",
dereverb_automatic=True,
model_id_bark="suno/bark-small",
model_id_coqui="tts_models/multilingual/multi-dataset/xtts_v2",
delete_previous_automatic=True,
):
remove_directory_contents("audio")
# Mapping speakers to voice variables
speaker_to_voice = {
"SPEAKER_00": tts_voice00,
"SPEAKER_01": tts_voice01,
"SPEAKER_02": tts_voice02,
"SPEAKER_03": tts_voice03,
"SPEAKER_04": tts_voice04,
"SPEAKER_05": tts_voice05,
"SPEAKER_06": tts_voice06,
"SPEAKER_07": tts_voice07,
"SPEAKER_08": tts_voice08,
"SPEAKER_09": tts_voice09,
"SPEAKER_10": tts_voice10,
"SPEAKER_11": tts_voice11,
}
# Assign 'SPEAKER_00' to segments without a 'speaker' key
for segment in result_diarize["segments"]:
if "speaker" not in segment:
segment["speaker"] = "SPEAKER_00"
logger.warning(
"NO SPEAKER DETECT IN SEGMENT: First TTS will be used in the"
f" segment time {segment['start'], segment['text']}"
)
# Assign the TTS name
segment["tts_name"] = speaker_to_voice[segment["speaker"]]
# Find TTS method
pattern_edge = re.compile(r".*-(Male|Female)$")
pattern_bark = re.compile(r".* BARK$")
pattern_vits = re.compile(r".* VITS$")
pattern_coqui = re.compile(r".+\.(wav|mp3|ogg|m4a)$")
pattern_vits_onnx = re.compile(r".* VITS-onnx$")
pattern_openai_tts = re.compile(r".* OpenAI-TTS$")
all_segments = result_diarize["segments"]
speakers_edge = find_spkr(pattern_edge, speaker_to_voice, all_segments)
speakers_bark = find_spkr(pattern_bark, speaker_to_voice, all_segments)
speakers_vits = find_spkr(pattern_vits, speaker_to_voice, all_segments)
speakers_coqui = find_spkr(pattern_coqui, speaker_to_voice, all_segments)
speakers_vits_onnx = find_spkr(
pattern_vits_onnx, speaker_to_voice, all_segments
)
speakers_openai_tts = find_spkr(
pattern_openai_tts, speaker_to_voice, all_segments
)
# Filter method in segments
filtered_edge = filter_by_speaker(speakers_edge, all_segments)
filtered_bark = filter_by_speaker(speakers_bark, all_segments)
filtered_vits = filter_by_speaker(speakers_vits, all_segments)
filtered_coqui = filter_by_speaker(speakers_coqui, all_segments)
filtered_vits_onnx = filter_by_speaker(speakers_vits_onnx, all_segments)
filtered_openai_tts = filter_by_speaker(speakers_openai_tts, all_segments)
# Infer
if filtered_edge["segments"]:
logger.info(f"EDGE TTS: {speakers_edge}")
segments_egde_tts(filtered_edge, TRANSLATE_AUDIO_TO, is_gui) # mp3
if filtered_bark["segments"]:
logger.info(f"BARK TTS: {speakers_bark}")
segments_bark_tts(
filtered_bark, TRANSLATE_AUDIO_TO, model_id_bark
) # wav
if filtered_vits["segments"]:
logger.info(f"VITS TTS: {speakers_vits}")
segments_vits_tts(filtered_vits, TRANSLATE_AUDIO_TO) # wav
if filtered_coqui["segments"]:
logger.info(f"Coqui TTS: {speakers_coqui}")
segments_coqui_tts(
filtered_coqui,
TRANSLATE_AUDIO_TO,
model_id_coqui,
speakers_coqui,
delete_previous_automatic,
dereverb_automatic,
) # wav
if filtered_vits_onnx["segments"]:
logger.info(f"PIPER TTS: {speakers_vits_onnx}")
segments_vits_onnx_tts(filtered_vits_onnx, TRANSLATE_AUDIO_TO) # wav
if filtered_openai_tts["segments"]:
logger.info(f"OpenAI TTS: {speakers_openai_tts}")
segments_openai_tts(filtered_openai_tts, TRANSLATE_AUDIO_TO) # wav
[result.pop("tts_name", None) for result in result_diarize["segments"]]
return [
speakers_edge,
speakers_bark,
speakers_vits,
speakers_coqui,
speakers_vits_onnx,
speakers_openai_tts
]
def accelerate_segments(
result_diarize,
max_accelerate_audio,
valid_speakers,
acceleration_rate_regulation=False,
folder_output="audio2",
):
logger.info("Apply acceleration")
(
speakers_edge,
speakers_bark,
speakers_vits,
speakers_coqui,
speakers_vits_onnx,
speakers_openai_tts
) = valid_speakers
create_directories(f"{folder_output}/audio/")
remove_directory_contents(f"{folder_output}/audio/")
audio_files = []
speakers_list = []
max_count_segments_idx = len(result_diarize["segments"]) - 1
for i, segment in tqdm(enumerate(result_diarize["segments"])):
text = segment["text"] # noqa
start = segment["start"]
end = segment["end"]
speaker = segment["speaker"]
# find name audio
# if speaker in speakers_edge:
filename = f"audio/{start}.ogg"
# elif speaker in speakers_bark + speakers_vits + speakers_coqui + speakers_vits_onnx:
# filename = f"audio/{start}.wav" # wav
# duration
duration_true = end - start
duration_tts = librosa.get_duration(filename=filename)
# Accelerate percentage
acc_percentage = duration_tts / duration_true
# Smoth
if acceleration_rate_regulation and acc_percentage >= 1.3:
try:
next_segment = result_diarize["segments"][
min(max_count_segments_idx, i + 1)
]
next_start = next_segment["start"]
next_speaker = next_segment["speaker"]
duration_with_next_start = next_start - start
if duration_with_next_start > duration_true:
extra_time = duration_with_next_start - duration_true
if speaker == next_speaker:
# half
smoth_duration = duration_true + (extra_time * 0.5)
else:
# 7/10
smoth_duration = duration_true + (extra_time * 0.7)
logger.debug(
f"Base acc: {acc_percentage}, "
f"smoth acc: {duration_tts / smoth_duration}"
)
acc_percentage = max(1.2, (duration_tts / smoth_duration))
except Exception as error:
logger.error(str(error))
if acc_percentage > max_accelerate_audio:
acc_percentage = max_accelerate_audio
elif acc_percentage <= 1.15 and acc_percentage >= 0.8:
acc_percentage = 1.0
elif acc_percentage <= 0.79:
acc_percentage = 0.8
# Round
acc_percentage = round(acc_percentage + 0.0, 1)
# Format read if need
if speaker in speakers_edge:
info_enc = sf.info(filename).format
else:
info_enc = "OGG"
# Apply aceleration or opposite to the audio file in folder_output folder
if acc_percentage == 1.0 and info_enc == "OGG":
copy_files(filename, f"{folder_output}{os.sep}audio")
else:
os.system(
f"ffmpeg -y -loglevel panic -i {filename} -filter:a atempo={acc_percentage} {folder_output}/{filename}"
)
if logger.isEnabledFor(logging.DEBUG):
duration_create = librosa.get_duration(
filename=f"{folder_output}/{filename}"
)
logger.debug(
f"acc_percen is {acc_percentage}, tts duration "
f"is {duration_tts}, new duration is {duration_create}"
f", for {filename}"
)
audio_files.append(f"{folder_output}/{filename}")
speaker = "TTS Speaker {:02d}".format(int(speaker[-2:]) + 1)
speakers_list.append(speaker)
return audio_files, speakers_list
# =====================================
# Tone color converter
# =====================================
def se_process_audio_segments(
source_seg, tone_color_converter, device, remove_previous_processed=True
):
# list wav seg
source_audio_segs = glob.glob(f"{source_seg}/*.wav")
if not source_audio_segs:
raise ValueError(
f"No audio segments found in {str(source_audio_segs)}"
)
source_se_path = os.path.join(source_seg, "se.pth")
# if exist not create wav
if os.path.isfile(source_se_path):
se = torch.load(source_se_path).to(device)
logger.debug(f"Previous created {source_se_path}")
else:
se = tone_color_converter.extract_se(source_audio_segs, source_se_path)
return se
def create_wav_vc(
valid_speakers,
segments_base,
audio_name,
max_segments=10,
target_dir="processed",
get_vocals_dereverb=False,
):
# valid_speakers = list({item['speaker'] for item in segments_base})
# Before function delete automatic delete_previous_automatic
output_dir = os.path.join(".", target_dir) # remove content
# remove_directory_contents(output_dir)
path_source_segments = []
path_target_segments = []
for speaker in valid_speakers:
filtered_speaker = [
segment
for segment in segments_base
if segment["speaker"] == speaker
]
if len(filtered_speaker) > 4:
filtered_speaker = filtered_speaker[1:]
dir_name_speaker = speaker + audio_name
dir_name_speaker_tts = "tts" + speaker + audio_name
dir_path_speaker = os.path.join(output_dir, dir_name_speaker)
dir_path_speaker_tts = os.path.join(output_dir, dir_name_speaker_tts)
create_directories([dir_path_speaker, dir_path_speaker_tts])
path_target_segments.append(dir_path_speaker)
path_source_segments.append(dir_path_speaker_tts)
# create wav
max_segments_count = 0
for seg in filtered_speaker:
duration = float(seg["end"]) - float(seg["start"])
if duration > 3.0 and duration < 18.0:
logger.info(
f'Processing segment: {seg["start"]}, {seg["end"]}, {seg["speaker"]}, {duration}, {seg["text"]}'
)
name_new_wav = str(seg["start"])
check_segment_audio_target_file = os.path.join(
dir_path_speaker, f"{name_new_wav}.wav"
)
if os.path.exists(check_segment_audio_target_file):
logger.debug(
"Segment vc source exists: "
f"{check_segment_audio_target_file}"
)
pass
else:
create_wav_file_vc(
sample_name=name_new_wav,
audio_wav="audio.wav",
start=(float(seg["start"]) + 1.0),
end=(float(seg["end"]) - 1.0),
output_final_path=dir_path_speaker,
get_vocals_dereverb=get_vocals_dereverb,
)
file_name_tts = f"audio2/audio/{str(seg['start'])}.ogg"
# copy_files(file_name_tts, os.path.join(output_dir, dir_name_speaker_tts)
convert_to_xtts_good_sample(
file_name_tts, dir_path_speaker_tts
)
max_segments_count += 1
if max_segments_count == max_segments:
break
if max_segments_count == 0:
logger.info("Taking the first segment")
seg = filtered_speaker[0]
logger.info(
f'Processing segment: {seg["start"]}, {seg["end"]}, {seg["speaker"]}, {seg["text"]}'
)
max_duration = float(seg["end"]) - float(seg["start"])
max_duration = max(1.0, min(max_duration, 18.0))
name_new_wav = str(seg["start"])
create_wav_file_vc(
sample_name=name_new_wav,
audio_wav="audio.wav",
start=(float(seg["start"])),
end=(float(seg["start"]) + max_duration),
output_final_path=dir_path_speaker,
get_vocals_dereverb=get_vocals_dereverb,
)
file_name_tts = f"audio2/audio/{str(seg['start'])}.ogg"
# copy_files(file_name_tts, os.path.join(output_dir, dir_name_speaker_tts)
convert_to_xtts_good_sample(file_name_tts, dir_path_speaker_tts)
logger.debug(f"Base: {str(path_source_segments)}")
logger.debug(f"Target: {str(path_target_segments)}")
return path_source_segments, path_target_segments
def toneconverter_openvoice(
result_diarize,
preprocessor_max_segments,
remove_previous_process=True,
get_vocals_dereverb=False,
model="openvoice",
):
audio_path = "audio.wav"
# se_path = "se.pth"
target_dir = "processed"
create_directories(target_dir)
from openvoice import se_extractor
from openvoice.api import ToneColorConverter
audio_name = f"{os.path.basename(audio_path).rsplit('.', 1)[0]}_{se_extractor.hash_numpy_array(audio_path)}"
# se_path = os.path.join(target_dir, audio_name, 'se.pth')
# create wav seg original and target
valid_speakers = list(
{item["speaker"] for item in result_diarize["segments"]}
)
logger.info("Openvoice preprocessor...")
if remove_previous_process:
remove_directory_contents(target_dir)
path_source_segments, path_target_segments = create_wav_vc(
valid_speakers,
result_diarize["segments"],
audio_name,
max_segments=preprocessor_max_segments,
get_vocals_dereverb=get_vocals_dereverb,
)
logger.info("Openvoice loading model...")
model_path_openvoice = "./OPENVOICE_MODELS"
url_model_openvoice = "https://huggingface.co./myshell-ai/OpenVoice/resolve/main/checkpoints/converter"
if "v2" in model:
model_path = os.path.join(model_path_openvoice, "v2")
url_model_openvoice = url_model_openvoice.replace(
"OpenVoice", "OpenVoiceV2"
).replace("checkpoints/", "")
else:
model_path = os.path.join(model_path_openvoice, "v1")
create_directories(model_path)
config_url = f"{url_model_openvoice}/config.json"
checkpoint_url = f"{url_model_openvoice}/checkpoint.pth"
config_path = download_manager(url=config_url, path=model_path)
checkpoint_path = download_manager(
url=checkpoint_url, path=model_path
)
device = os.environ.get("SONITR_DEVICE")
tone_color_converter = ToneColorConverter(config_path, device=device)
tone_color_converter.load_ckpt(checkpoint_path)
logger.info("Openvoice tone color converter:")
global_progress_bar = tqdm(total=len(result_diarize["segments"]), desc="Progress")
for source_seg, target_seg, speaker in zip(
path_source_segments, path_target_segments, valid_speakers
):
# source_se_path = os.path.join(source_seg, 'se.pth')
source_se = se_process_audio_segments(source_seg, tone_color_converter, device)
# target_se_path = os.path.join(target_seg, 'se.pth')
target_se = se_process_audio_segments(target_seg, tone_color_converter, device)
# Iterate throw segments
encode_message = "@MyShell"
filtered_speaker = [
segment
for segment in result_diarize["segments"]
if segment["speaker"] == speaker
]
for seg in filtered_speaker:
src_path = (
save_path
) = f"audio2/audio/{str(seg['start'])}.ogg" # overwrite
logger.debug(f"{src_path}")
tone_color_converter.convert(
audio_src_path=src_path,
src_se=source_se,
tgt_se=target_se,
output_path=save_path,
message=encode_message,
)
global_progress_bar.update(1)
global_progress_bar.close()
try:
del tone_color_converter
gc.collect()
torch.cuda.empty_cache()
except Exception as error:
logger.error(str(error))
gc.collect()
torch.cuda.empty_cache()
def toneconverter_freevc(
result_diarize,
remove_previous_process=True,
get_vocals_dereverb=False,
):
audio_path = "audio.wav"
target_dir = "processed"
create_directories(target_dir)
from openvoice import se_extractor
audio_name = f"{os.path.basename(audio_path).rsplit('.', 1)[0]}_{se_extractor.hash_numpy_array(audio_path)}"
# create wav seg; original is target and dubbing is source
valid_speakers = list(
{item["speaker"] for item in result_diarize["segments"]}
)
logger.info("FreeVC preprocessor...")
if remove_previous_process:
remove_directory_contents(target_dir)
path_source_segments, path_target_segments = create_wav_vc(
valid_speakers,
result_diarize["segments"],
audio_name,
max_segments=1,
get_vocals_dereverb=get_vocals_dereverb,
)
logger.info("FreeVC loading model...")
device_id = os.environ.get("SONITR_DEVICE")
device = None if device_id == "cpu" else device_id
try:
from TTS.api import TTS
tts = TTS(
model_name="voice_conversion_models/multilingual/vctk/freevc24",
progress_bar=False
).to(device)
except Exception as error:
logger.error(str(error))
logger.error("Error loading the FreeVC model.")
return
logger.info("FreeVC process:")
global_progress_bar = tqdm(total=len(result_diarize["segments"]), desc="Progress")
for source_seg, target_seg, speaker in zip(
path_source_segments, path_target_segments, valid_speakers
):
filtered_speaker = [
segment
for segment in result_diarize["segments"]
if segment["speaker"] == speaker
]
files_and_directories = os.listdir(target_seg)
wav_files = [file for file in files_and_directories if file.endswith(".wav")]
original_wav_audio_segment = os.path.join(target_seg, wav_files[0])
for seg in filtered_speaker:
src_path = (
save_path
) = f"audio2/audio/{str(seg['start'])}.ogg" # overwrite
logger.debug(f"{src_path} - {original_wav_audio_segment}")
wav = tts.voice_conversion(
source_wav=src_path,
target_wav=original_wav_audio_segment,
)
sf.write(
file=save_path,
samplerate=tts.voice_converter.vc_config.audio.output_sample_rate,
data=wav,
format="ogg",
subtype="vorbis",
)
global_progress_bar.update(1)
global_progress_bar.close()
try:
del tts
gc.collect()
torch.cuda.empty_cache()
except Exception as error:
logger.error(str(error))
gc.collect()
torch.cuda.empty_cache()
def toneconverter(
result_diarize,
preprocessor_max_segments,
remove_previous_process=True,
get_vocals_dereverb=False,
method_vc="freevc"
):
if method_vc == "freevc":
if preprocessor_max_segments > 1:
logger.info("FreeVC only uses one segment.")
return toneconverter_freevc(
result_diarize,
remove_previous_process=remove_previous_process,
get_vocals_dereverb=get_vocals_dereverb,
)
elif "openvoice" in method_vc:
return toneconverter_openvoice(
result_diarize,
preprocessor_max_segments,
remove_previous_process=remove_previous_process,
get_vocals_dereverb=get_vocals_dereverb,
model=method_vc,
)
if __name__ == "__main__":
from segments import result_diarize
audio_segmentation_to_voice(
result_diarize,
TRANSLATE_AUDIO_TO="en",
max_accelerate_audio=2.1,
is_gui=True,
tts_voice00="en-facebook-mms VITS",
tts_voice01="en-CA-ClaraNeural-Female",
tts_voice02="en-GB-ThomasNeural-Male",
tts_voice03="en-GB-SoniaNeural-Female",
tts_voice04="en-NZ-MitchellNeural-Male",
tts_voice05="en-GB-MaisieNeural-Female",
)
|