forked from mrq/ai-voice-cloning
Merge branch 'master' of https://git.ecker.tech/ben_mkiv/ai-voice-cloning
This commit is contained in:
commit
1ec3344999
|
|
@ -1,8 +1,8 @@
|
|||
# AI Voice Cloning
|
||||
|
||||
This [repo](https://git.ecker.tech/mrq/ai-voice-cloning)/[rentry](https://rentry.org/AI-Voice-Cloning/) aims to serve as both a foolproof guide for setting up AI voice cloning tools for legitimate, local use on Windows/Linux, as well as a stepping stone for anons that genuinely want to play around with [TorToiSe](https://github.com/neonbjb/tortoise-tts).
|
||||
> **Note** This project has been in dire need of being rewritten from the ground up for some time. Apologies for any crust from my rather spaghetti code.
|
||||
|
||||
Similar to my own findings for Stable Diffusion image generation, this rentry may appear a little disheveled as I note my new findings with TorToiSe. Please keep this in mind if the guide seems to shift a bit or sound confusing.
|
||||
This [repo](https://git.ecker.tech/mrq/ai-voice-cloning)/[rentry](https://rentry.org/AI-Voice-Cloning/) aims to serve as both a foolproof guide for setting up AI voice cloning tools for legitimate, local use on Windows/Linux, as well as a stepping stone for anons that genuinely want to play around with [TorToiSe](https://github.com/neonbjb/tortoise-tts).
|
||||
|
||||
>\>Ugh... why bother when I can just abuse 11.AI?
|
||||
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
Subproject commit 5ff00bf3bfa97e2c8e9f166b920273f83ac9d8f0
|
||||
Subproject commit b10c58436d6871c26485d30b203e6cfdd4167602
|
||||
|
|
@ -8,3 +8,4 @@ voicefixer
|
|||
psutil
|
||||
phonemizer
|
||||
pydantic==1.10.11
|
||||
websockets
|
||||
316
src/utils.py
316
src/utils.py
|
|
@ -45,7 +45,7 @@ from tortoise.utils.device import get_device_name, set_device_name, get_device_c
|
|||
|
||||
MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
|
||||
|
||||
WHISPER_MODELS = ["tiny", "base", "small", "medium", "large"]
|
||||
WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v1", "large-v2"]
|
||||
WHISPER_SPECIALIZED_MODELS = ["tiny.en", "base.en", "small.en", "medium.en"]
|
||||
WHISPER_BACKENDS = ["openai/whisper", "lightmare/whispercpp", "m-bain/whisperx"]
|
||||
VOCODERS = ['univnet', 'bigvgan_base_24khz_100band', 'bigvgan_24khz_100band']
|
||||
|
|
@ -61,12 +61,15 @@ RESAMPLERS = {}
|
|||
|
||||
MIN_TRAINING_DURATION = 0.6
|
||||
MAX_TRAINING_DURATION = 11.6097505669
|
||||
MAX_TRAINING_CHAR_LENGTH = 200
|
||||
|
||||
VALLE_ENABLED = False
|
||||
BARK_ENABLED = False
|
||||
|
||||
VERBOSE_DEBUG = True
|
||||
|
||||
import traceback
|
||||
|
||||
try:
|
||||
from whisper.normalizers.english import EnglishTextNormalizer
|
||||
from whisper.normalizers.basic import BasicTextNormalizer
|
||||
|
|
@ -75,7 +78,7 @@ try:
|
|||
print("Whisper detected")
|
||||
except Exception as e:
|
||||
if VERBOSE_DEBUG:
|
||||
print("Error:", e)
|
||||
print(traceback.format_exc())
|
||||
pass
|
||||
|
||||
try:
|
||||
|
|
@ -90,12 +93,14 @@ try:
|
|||
VALLE_ENABLED = True
|
||||
except Exception as e:
|
||||
if VERBOSE_DEBUG:
|
||||
print("Error:", e)
|
||||
print(traceback.format_exc())
|
||||
pass
|
||||
|
||||
if VALLE_ENABLED:
|
||||
TTSES.append('vall-e')
|
||||
|
||||
# torchaudio.set_audio_backend('soundfile')
|
||||
|
||||
try:
|
||||
import bark
|
||||
from bark import text_to_semantic
|
||||
|
|
@ -109,35 +114,10 @@ try:
|
|||
BARK_ENABLED = True
|
||||
except Exception as e:
|
||||
if VERBOSE_DEBUG:
|
||||
print("Error:", e)
|
||||
print(traceback.format_exc())
|
||||
pass
|
||||
|
||||
if BARK_ENABLED:
|
||||
try:
|
||||
from vocos import Vocos
|
||||
VOCOS_ENABLED = True
|
||||
print("Vocos detected")
|
||||
except Exception as e:
|
||||
if VERBOSE_DEBUG:
|
||||
print("Error:", e)
|
||||
VOCOS_ENABLED = False
|
||||
|
||||
try:
|
||||
from hubert.hubert_manager import HuBERTManager
|
||||
from hubert.pre_kmeans_hubert import CustomHubert
|
||||
from hubert.customtokenizer import CustomTokenizer
|
||||
|
||||
hubert_manager = HuBERTManager()
|
||||
hubert_manager.make_sure_hubert_installed()
|
||||
hubert_manager.make_sure_tokenizer_installed()
|
||||
|
||||
HUBERT_ENABLED = True
|
||||
print("HuBERT detected")
|
||||
except Exception as e:
|
||||
if VERBOSE_DEBUG:
|
||||
print("Error:", e)
|
||||
HUBERT_ENABLED = False
|
||||
|
||||
TTSES.append('bark')
|
||||
|
||||
def semantic_to_audio_tokens(
|
||||
|
|
@ -181,7 +161,32 @@ if BARK_ENABLED:
|
|||
|
||||
self.device = get_device_name()
|
||||
|
||||
if VOCOS_ENABLED:
|
||||
try:
|
||||
from vocos import Vocos
|
||||
self.vocos_enabled = True
|
||||
print("Vocos detected")
|
||||
except Exception as e:
|
||||
if VERBOSE_DEBUG:
|
||||
print(traceback.format_exc())
|
||||
self.vocos_enabled = False
|
||||
|
||||
try:
|
||||
from hubert.hubert_manager import HuBERTManager
|
||||
from hubert.pre_kmeans_hubert import CustomHubert
|
||||
from hubert.customtokenizer import CustomTokenizer
|
||||
|
||||
hubert_manager = HuBERTManager()
|
||||
hubert_manager.make_sure_hubert_installed()
|
||||
hubert_manager.make_sure_tokenizer_installed()
|
||||
|
||||
self.hubert_enabled = True
|
||||
print("HuBERT detected")
|
||||
except Exception as e:
|
||||
if VERBOSE_DEBUG:
|
||||
print(traceback.format_exc())
|
||||
self.hubert_enabled = False
|
||||
|
||||
if self.vocos_enabled:
|
||||
self.vocos = Vocos.from_pretrained("charactr/vocos-encodec-24khz").to(self.device)
|
||||
|
||||
def create_voice( self, voice ):
|
||||
|
|
@ -238,7 +243,7 @@ if BARK_ENABLED:
|
|||
|
||||
# generate semantic tokens
|
||||
|
||||
if HUBERT_ENABLED:
|
||||
if self.hubert_enabled:
|
||||
wav = wav.to(self.device)
|
||||
|
||||
# Extract discrete codes from EnCodec
|
||||
|
|
@ -426,7 +431,7 @@ def generate_bark(**kwargs):
|
|||
idx_cache = {}
|
||||
for i, file in enumerate(os.listdir(outdir)):
|
||||
filename = os.path.basename(file)
|
||||
extension = os.path.splitext(filename)[1]
|
||||
extension = os.path.splitext(filename)[-1][1:]
|
||||
if extension != ".json" and extension != ".wav":
|
||||
continue
|
||||
match = re.findall(rf"^{cleanup_voice_name(voice)}_(\d+)(?:.+?)?{extension}$", filename)
|
||||
|
|
@ -672,18 +677,23 @@ def generate_valle(**kwargs):
|
|||
|
||||
voice_cache = {}
|
||||
def fetch_voice( voice ):
|
||||
if voice in voice_cache:
|
||||
return voice_cache[voice]
|
||||
voice_dir = f'./training/{voice}/audio/'
|
||||
if not os.path.isdir(voice_dir):
|
||||
|
||||
if not os.path.isdir(voice_dir) or len(os.listdir(voice_dir)) == 0:
|
||||
voice_dir = f'./voices/{voice}/'
|
||||
|
||||
files = [ f'{voice_dir}/{d}' for d in os.listdir(voice_dir) if d[-4:] == ".wav" ]
|
||||
# return files
|
||||
return random.choice(files)
|
||||
voice_cache[voice] = random.choice(files)
|
||||
return voice_cache[voice]
|
||||
|
||||
def get_settings( override=None ):
|
||||
settings = {
|
||||
'ar_temp': float(parameters['temperature']),
|
||||
'nar_temp': float(parameters['temperature']),
|
||||
'max_ar_samples': parameters['num_autoregressive_samples'],
|
||||
'max_ar_steps': parameters['num_autoregressive_samples'],
|
||||
}
|
||||
|
||||
# could be better to just do a ternary on everything above, but i am not a professional
|
||||
|
|
@ -697,7 +707,7 @@ def generate_valle(**kwargs):
|
|||
continue
|
||||
settings[k] = override[k]
|
||||
|
||||
settings['reference'] = fetch_voice(voice=selected_voice)
|
||||
settings['references'] = [ fetch_voice(voice=selected_voice) for _ in range(3) ]
|
||||
return settings
|
||||
|
||||
if not parameters['delimiter']:
|
||||
|
|
@ -723,7 +733,7 @@ def generate_valle(**kwargs):
|
|||
idx_cache = {}
|
||||
for i, file in enumerate(os.listdir(outdir)):
|
||||
filename = os.path.basename(file)
|
||||
extension = os.path.splitext(filename)[1]
|
||||
extension = os.path.splitext(filename)[-1][1:]
|
||||
if extension != ".json" and extension != ".wav":
|
||||
continue
|
||||
match = re.findall(rf"^{voice}_(\d+)(?:.+?)?{extension}$", filename)
|
||||
|
|
@ -783,11 +793,14 @@ def generate_valle(**kwargs):
|
|||
except Exception as e:
|
||||
raise Exception("Prompt settings editing requested, but received invalid JSON")
|
||||
|
||||
settings = get_settings( override=override )
|
||||
reference = settings['reference']
|
||||
settings.pop("reference")
|
||||
name = get_name(line=line, candidate=0)
|
||||
|
||||
gen = tts.inference(cut_text, reference, **settings )
|
||||
settings = get_settings( override=override )
|
||||
references = settings['references']
|
||||
settings.pop("references")
|
||||
settings['out_path'] = f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav'
|
||||
|
||||
gen = tts.inference(cut_text, references, **settings )
|
||||
|
||||
run_time = time.time()-start_time
|
||||
print(f"Generating line took {run_time} seconds")
|
||||
|
|
@ -805,7 +818,7 @@ def generate_valle(**kwargs):
|
|||
|
||||
# save here in case some error happens mid-batch
|
||||
#torchaudio.save(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', wav.cpu(), sr)
|
||||
soundfile.write(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', wav.cpu()[0,0], sr)
|
||||
#soundfile.write(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', wav.cpu()[0,0], sr)
|
||||
wav, sr = torchaudio.load(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav')
|
||||
|
||||
audio_cache[name] = {
|
||||
|
|
@ -1085,7 +1098,7 @@ def generate_tortoise(**kwargs):
|
|||
idx_cache = {}
|
||||
for i, file in enumerate(os.listdir(outdir)):
|
||||
filename = os.path.basename(file)
|
||||
extension = os.path.splitext(filename)[1]
|
||||
extension = os.path.splitext(filename)[-1][1:]
|
||||
if extension != ".json" and extension != ".wav":
|
||||
continue
|
||||
match = re.findall(rf"^{voice}_(\d+)(?:.+?)?{extension}$", filename)
|
||||
|
|
@ -1605,30 +1618,18 @@ class TrainingState():
|
|||
if args.tts_backend == "vall-e":
|
||||
keys['lrs'] = [
|
||||
'ar.lr', 'nar.lr',
|
||||
'ar-half.lr', 'nar-half.lr',
|
||||
'ar-quarter.lr', 'nar-quarter.lr',
|
||||
]
|
||||
keys['losses'] = [
|
||||
'ar.loss', 'nar.loss', 'ar+nar.loss',
|
||||
'ar-half.loss', 'nar-half.loss', 'ar-half+nar-half.loss',
|
||||
'ar-quarter.loss', 'nar-quarter.loss', 'ar-quarter+nar-quarter.loss',
|
||||
|
||||
# 'ar.loss', 'nar.loss', 'ar+nar.loss',
|
||||
'ar.loss.nll', 'nar.loss.nll',
|
||||
'ar-half.loss.nll', 'nar-half.loss.nll',
|
||||
'ar-quarter.loss.nll', 'nar-quarter.loss.nll',
|
||||
]
|
||||
|
||||
keys['accuracies'] = [
|
||||
'ar.loss.acc', 'nar.loss.acc',
|
||||
'ar-half.loss.acc', 'nar-half.loss.acc',
|
||||
'ar-quarter.loss.acc', 'nar-quarter.loss.acc',
|
||||
'ar.stats.acc', 'nar.loss.acc',
|
||||
]
|
||||
keys['precisions'] = [
|
||||
'ar.loss.precision', 'nar.loss.precision',
|
||||
'ar-half.loss.precision', 'nar-half.loss.precision',
|
||||
'ar-quarter.loss.precision', 'nar-quarter.loss.precision',
|
||||
]
|
||||
keys['grad_norms'] = ['ar.grad_norm', 'nar.grad_norm', 'ar-half.grad_norm', 'nar-half.grad_norm', 'ar-quarter.grad_norm', 'nar-quarter.grad_norm']
|
||||
keys['precisions'] = [ 'ar.loss.precision', 'nar.loss.precision', ]
|
||||
keys['grad_norms'] = ['ar.grad_norm', 'nar.grad_norm']
|
||||
|
||||
for k in keys['lrs']:
|
||||
if k not in self.info:
|
||||
|
|
@ -1746,7 +1747,8 @@ class TrainingState():
|
|||
if args.tts_backend == "tortoise":
|
||||
logs = sorted([f'{self.training_dir}/finetune/{d}' for d in os.listdir(f'{self.training_dir}/finetune/') if d[-4:] == ".log" ])
|
||||
else:
|
||||
logs = sorted([f'{self.training_dir}/logs/{d}/log.txt' for d in os.listdir(f'{self.training_dir}/logs/') ])
|
||||
log_dir = "logs"
|
||||
logs = sorted([f'{self.training_dir}/{log_dir}/{d}/log.txt' for d in os.listdir(f'{self.training_dir}/{log_dir}/') ])
|
||||
|
||||
if update:
|
||||
logs = [logs[-1]]
|
||||
|
|
@ -2220,6 +2222,8 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
|
|||
indir = f'./training/{voice}/'
|
||||
infile = f'{indir}/whisper.json'
|
||||
|
||||
quantize_in_memory = args.tts_backend == "vall-e"
|
||||
|
||||
os.makedirs(f'{indir}/audio/', exist_ok=True)
|
||||
|
||||
TARGET_SAMPLE_RATE = 22050
|
||||
|
|
@ -2245,13 +2249,24 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
|
|||
continue
|
||||
|
||||
results[basename] = result
|
||||
|
||||
if not quantize_in_memory:
|
||||
waveform, sample_rate = torchaudio.load(file)
|
||||
# resample to the input rate, since it'll get resampled for training anyways
|
||||
# this should also "help" increase throughput a bit when filling the dataloaders
|
||||
waveform, sample_rate = resample(waveform, sample_rate, TARGET_SAMPLE_RATE)
|
||||
if waveform.shape[0] == 2:
|
||||
waveform = waveform[:1]
|
||||
torchaudio.save(f"{indir}/audio/{basename}", waveform, sample_rate, encoding="PCM_S", bits_per_sample=16)
|
||||
|
||||
try:
|
||||
kwargs = {}
|
||||
if basename[-4:] == ".wav":
|
||||
kwargs['encoding'] = "PCM_S"
|
||||
kwargs['bits_per_sample'] = 16
|
||||
|
||||
torchaudio.save(f"{indir}/audio/{basename}", waveform, sample_rate, **kwargs)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
with open(infile, 'w', encoding="utf-8") as f:
|
||||
f.write(json.dumps(results, indent='\t'))
|
||||
|
|
@ -2317,6 +2332,9 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
|
|||
segments = 0
|
||||
for filename in results:
|
||||
path = f'./voices/{voice}/{filename}'
|
||||
extension = os.path.splitext(filename)[-1][1:]
|
||||
out_extension = extension # "wav"
|
||||
|
||||
if not os.path.exists(path):
|
||||
path = f'./training/{voice}/{filename}'
|
||||
|
||||
|
|
@ -2333,7 +2351,7 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
|
|||
duration = num_frames / sample_rate
|
||||
|
||||
for segment in result['segments']:
|
||||
file = filename.replace(".wav", f"_{pad(segment['id'], 4)}.wav")
|
||||
file = filename.replace(f".{extension}", f"_{pad(segment['id'], 4)}.{out_extension}")
|
||||
|
||||
sliced, error = slice_waveform( waveform, sample_rate, segment['start'] + start_offset, segment['end'] + end_offset, trim_silence )
|
||||
if error:
|
||||
|
|
@ -2341,12 +2359,18 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
|
|||
print(message)
|
||||
messages.append(message)
|
||||
continue
|
||||
|
||||
sliced, _ = resample( sliced, sample_rate, TARGET_SAMPLE_RATE )
|
||||
|
||||
if waveform.shape[0] == 2:
|
||||
waveform = waveform[:1]
|
||||
|
||||
torchaudio.save(f"{indir}/audio/{file}", sliced, TARGET_SAMPLE_RATE, encoding="PCM_S", bits_per_sample=16)
|
||||
kwargs = {}
|
||||
if file[-4:] == ".wav":
|
||||
kwargs['encoding'] = "PCM_S"
|
||||
kwargs['bits_per_sample'] = 16
|
||||
|
||||
torchaudio.save(f"{indir}/audio/{file}", sliced, TARGET_SAMPLE_RATE, **kwargs)
|
||||
|
||||
segments +=1
|
||||
|
||||
|
|
@ -2462,18 +2486,32 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
|
|||
|
||||
errored = 0
|
||||
messages = []
|
||||
normalize = True
|
||||
normalize = False # True
|
||||
phonemize = should_phonemize()
|
||||
lines = { 'training': [], 'validation': [] }
|
||||
segments = {}
|
||||
|
||||
quantize_in_memory = args.tts_backend == "vall-e"
|
||||
|
||||
if args.tts_backend != "tortoise":
|
||||
text_length = 0
|
||||
audio_length = 0
|
||||
|
||||
start_offset = -0.1
|
||||
end_offset = 0.1
|
||||
trim_silence = False
|
||||
|
||||
TARGET_SAMPLE_RATE = 22050
|
||||
if args.tts_backend != "tortoise":
|
||||
TARGET_SAMPLE_RATE = 24000
|
||||
if tts:
|
||||
TARGET_SAMPLE_RATE = tts.input_sample_rate
|
||||
|
||||
for filename in tqdm(results, desc="Parsing results"):
|
||||
use_segment = use_segments
|
||||
|
||||
extension = os.path.splitext(filename)[-1][1:]
|
||||
out_extension = extension # "wav"
|
||||
result = results[filename]
|
||||
lang = result['language']
|
||||
language = LANGUAGES[lang] if lang in LANGUAGES else lang
|
||||
|
|
@ -2481,8 +2519,8 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
|
|||
|
||||
# check if unsegmented text exceeds 200 characters
|
||||
if not use_segment:
|
||||
if len(result['text']) > 200:
|
||||
message = f"Text length too long (200 < {len(result['text'])}), using segments: {filename}"
|
||||
if len(result['text']) > MAX_TRAINING_CHAR_LENGTH:
|
||||
message = f"Text length too long ({MAX_TRAINING_CHAR_LENGTH} < {len(result['text'])}), using segments: {filename}"
|
||||
print(message)
|
||||
messages.append(message)
|
||||
use_segment = True
|
||||
|
|
@ -2490,13 +2528,15 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
|
|||
# check if unsegmented audio exceeds 11.6s
|
||||
if not use_segment:
|
||||
path = f'{indir}/audio/{filename}'
|
||||
if not os.path.exists(path):
|
||||
if not quantize_in_memory and not os.path.exists(path):
|
||||
messages.append(f"Missing source audio: {filename}")
|
||||
errored += 1
|
||||
continue
|
||||
|
||||
metadata = torchaudio.info(path)
|
||||
duration = metadata.num_frames / metadata.sample_rate
|
||||
duration = 0
|
||||
for segment in result['segments']:
|
||||
duration = max(duration, result['segments'][segment]['end'])
|
||||
|
||||
if duration >= MAX_TRAINING_DURATION:
|
||||
message = f"Audio too large, using segments: {filename}"
|
||||
print(message)
|
||||
|
|
@ -2511,13 +2551,13 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
|
|||
if duration <= MIN_TRAINING_DURATION or MAX_TRAINING_DURATION <= duration:
|
||||
continue
|
||||
|
||||
path = f'{indir}/audio/' + filename.replace(".wav", f"_{pad(segment['id'], 4)}.wav")
|
||||
path = f'{indir}/audio/' + filename.replace(f".{extension}", f"_{pad(segment['id'], 4)}.{out_extension}")
|
||||
if os.path.exists(path):
|
||||
continue
|
||||
exists = False
|
||||
break
|
||||
|
||||
if not exists:
|
||||
if not quantize_in_memory and not exists:
|
||||
tmp = {}
|
||||
tmp[filename] = result
|
||||
print(f"Audio not segmented, segmenting: {filename}")
|
||||
|
|
@ -2525,6 +2565,23 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
|
|||
print(message)
|
||||
messages = messages + message.split("\n")
|
||||
|
||||
waveform = None
|
||||
|
||||
|
||||
if quantize_in_memory:
|
||||
path = f'{indir}/audio/{filename}'
|
||||
if not os.path.exists(path):
|
||||
path = f'./voices/{voice}/{filename}'
|
||||
|
||||
if not os.path.exists(path):
|
||||
message = f"Audio not found: {path}"
|
||||
print(message)
|
||||
messages.append(message)
|
||||
#continue
|
||||
else:
|
||||
waveform = torchaudio.load(path)
|
||||
waveform = resample(waveform[0], waveform[1], TARGET_SAMPLE_RATE)
|
||||
|
||||
if not use_segment:
|
||||
segments[filename] = {
|
||||
'text': result['text'],
|
||||
|
|
@ -2533,13 +2590,18 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
|
|||
'normalizer': normalizer,
|
||||
'phonemes': result['phonemes'] if 'phonemes' in result else None
|
||||
}
|
||||
|
||||
if waveform:
|
||||
segments[filename]['waveform'] = waveform
|
||||
else:
|
||||
for segment in result['segments']:
|
||||
duration = segment['end'] - segment['start']
|
||||
if duration <= MIN_TRAINING_DURATION or MAX_TRAINING_DURATION <= duration:
|
||||
continue
|
||||
|
||||
segments[filename.replace(".wav", f"_{pad(segment['id'], 4)}.wav")] = {
|
||||
file = filename.replace(f".{extension}", f"_{pad(segment['id'], 4)}.{out_extension}")
|
||||
|
||||
segments[file] = {
|
||||
'text': segment['text'],
|
||||
'lang': lang,
|
||||
'language': language,
|
||||
|
|
@ -2547,22 +2609,27 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
|
|||
'phonemes': segment['phonemes'] if 'phonemes' in segment else None
|
||||
}
|
||||
|
||||
if waveform:
|
||||
sliced, error = slice_waveform( waveform[0], waveform[1], segment['start'] + start_offset, segment['end'] + end_offset, trim_silence )
|
||||
if error:
|
||||
message = f"{error}, skipping... {file}"
|
||||
print(message)
|
||||
messages.append(message)
|
||||
segments[file]['error'] = error
|
||||
#continue
|
||||
else:
|
||||
segments[file]['waveform'] = (sliced, waveform[1])
|
||||
|
||||
jobs = {
|
||||
'quantize': [[], []],
|
||||
'phonemize': [[], []],
|
||||
}
|
||||
|
||||
for file in tqdm(segments, desc="Parsing segments"):
|
||||
extension = os.path.splitext(file)[-1][1:]
|
||||
result = segments[file]
|
||||
path = f'{indir}/audio/{file}'
|
||||
|
||||
if not os.path.exists(path):
|
||||
message = f"Missing segment, skipping... {file}"
|
||||
print(message)
|
||||
messages.append(message)
|
||||
errored += 1
|
||||
continue
|
||||
|
||||
text = result['text']
|
||||
lang = result['lang']
|
||||
language = result['language']
|
||||
|
|
@ -2573,28 +2640,20 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
|
|||
|
||||
normalized = normalizer(text) if normalize else text
|
||||
|
||||
if len(text) > 200:
|
||||
message = f"Text length too long (200 < {len(text)}), skipping... {file}"
|
||||
if len(text) > MAX_TRAINING_CHAR_LENGTH:
|
||||
message = f"Text length too long ({MAX_TRAINING_CHAR_LENGTH} < {len(text)}), skipping... {file}"
|
||||
print(message)
|
||||
messages.append(message)
|
||||
errored += 1
|
||||
continue
|
||||
|
||||
waveform, sample_rate = torchaudio.load(path)
|
||||
num_channels, num_frames = waveform.shape
|
||||
duration = num_frames / sample_rate
|
||||
# num_channels, num_frames = waveform.shape
|
||||
#duration = num_frames / sample_rate
|
||||
|
||||
error = validate_waveform( waveform, sample_rate )
|
||||
if error:
|
||||
message = f"{error}, skipping... {file}"
|
||||
print(message)
|
||||
messages.append(message)
|
||||
errored += 1
|
||||
continue
|
||||
|
||||
culled = len(text) < text_length
|
||||
if not culled and audio_length > 0:
|
||||
culled = duration < audio_length
|
||||
#if not culled and audio_length > 0:
|
||||
# culled = duration < audio_length
|
||||
|
||||
line = f'audio/{file}|{phonemes if phonemize and phonemes else text}'
|
||||
|
||||
|
|
@ -2605,17 +2664,8 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
|
|||
|
||||
os.makedirs(f'{indir}/valle/', exist_ok=True)
|
||||
|
||||
qnt_file = f'{indir}/valle/{file.replace(".wav",".qnt.pt")}'
|
||||
if not os.path.exists(qnt_file):
|
||||
jobs['quantize'][0].append(qnt_file)
|
||||
jobs['quantize'][1].append((waveform, sample_rate))
|
||||
"""
|
||||
quantized = valle_quantize( waveform, sample_rate ).cpu()
|
||||
torch.save(quantized, f'{indir}/valle/{file.replace(".wav",".qnt.pt")}')
|
||||
print("Quantized:", file)
|
||||
"""
|
||||
|
||||
phn_file = f'{indir}/valle/{file.replace(".wav",".phn.txt")}'
|
||||
#phn_file = f'{indir}/valle/{file.replace(f".{extension}",".phn.txt")}'
|
||||
phn_file = f'./training/valle/data/{voice}/{file.replace(f".{extension}",".phn.txt")}'
|
||||
if not os.path.exists(phn_file):
|
||||
jobs['phonemize'][0].append(phn_file)
|
||||
jobs['phonemize'][1].append(normalized)
|
||||
|
|
@ -2625,13 +2675,46 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
|
|||
print("Phonemized:", file, normalized, text)
|
||||
"""
|
||||
|
||||
#qnt_file = f'{indir}/valle/{file.replace(f".{extension}",".qnt.pt")}'
|
||||
qnt_file = f'./training/valle/data/{voice}/{file.replace(f".{extension}",".qnt.pt")}'
|
||||
if 'error' not in result:
|
||||
if not quantize_in_memory and not os.path.exists(path):
|
||||
message = f"Missing segment, skipping... {file}"
|
||||
print(message)
|
||||
messages.append(message)
|
||||
errored += 1
|
||||
continue
|
||||
|
||||
if not os.path.exists(qnt_file):
|
||||
waveform = None
|
||||
if 'waveform' in result:
|
||||
waveform, sample_rate = result['waveform']
|
||||
elif os.path.exists(path):
|
||||
waveform, sample_rate = torchaudio.load(path)
|
||||
error = validate_waveform( waveform, sample_rate )
|
||||
if error:
|
||||
message = f"{error}, skipping... {file}"
|
||||
print(message)
|
||||
messages.append(message)
|
||||
errored += 1
|
||||
continue
|
||||
|
||||
if waveform is not None:
|
||||
jobs['quantize'][0].append(qnt_file)
|
||||
jobs['quantize'][1].append((waveform, sample_rate))
|
||||
"""
|
||||
quantized = valle_quantize( waveform, sample_rate ).cpu()
|
||||
torch.save(quantized, f'{indir}/valle/{file.replace(".wav",".qnt.pt")}')
|
||||
print("Quantized:", file)
|
||||
"""
|
||||
|
||||
for i in tqdm(range(len(jobs['quantize'][0])), desc="Quantizing"):
|
||||
qnt_file = jobs['quantize'][0][i]
|
||||
waveform, sample_rate = jobs['quantize'][1][i]
|
||||
|
||||
quantized = valle_quantize( waveform, sample_rate ).cpu()
|
||||
torch.save(quantized, qnt_file)
|
||||
print("Quantized:", qnt_file)
|
||||
#print("Quantized:", qnt_file)
|
||||
|
||||
for i in tqdm(range(len(jobs['phonemize'][0])), desc="Phonemizing"):
|
||||
phn_file = jobs['phonemize'][0][i]
|
||||
|
|
@ -2640,7 +2723,7 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
|
|||
try:
|
||||
phonemized = valle_phonemize( normalized )
|
||||
open(phn_file, 'w', encoding='utf-8').write(" ".join(phonemized))
|
||||
print("Phonemized:", phn_file)
|
||||
#print("Phonemized:", phn_file)
|
||||
except Exception as e:
|
||||
message = f"Failed to phonemize: {phn_file}: {normalized}"
|
||||
messages.append(message)
|
||||
|
|
@ -2970,17 +3053,26 @@ def import_voices(files, saveAs=None, progress=None):
|
|||
def relative_paths( dirs ):
|
||||
return [ './' + os.path.relpath( d ).replace("\\", "/") for d in dirs ]
|
||||
|
||||
def get_voice( name, dir=get_voice_dir(), load_latents=True ):
|
||||
def get_voice( name, dir=get_voice_dir(), load_latents=True, extensions=["wav", "mp3", "flac"] ):
|
||||
subj = f'{dir}/{name}/'
|
||||
if not os.path.isdir(subj):
|
||||
return
|
||||
files = os.listdir(subj)
|
||||
|
||||
voice = list(glob(f'{subj}/*.wav')) + list(glob(f'{subj}/*.mp3')) + list(glob(f'{subj}/*.flac'))
|
||||
if load_latents:
|
||||
voice = voice + list(glob(f'{subj}/*.pth'))
|
||||
extensions.append("pth")
|
||||
|
||||
voice = []
|
||||
for file in files:
|
||||
ext = os.path.splitext(file)[-1][1:]
|
||||
if ext not in extensions:
|
||||
continue
|
||||
|
||||
voice.append(f'{subj}/{file}')
|
||||
|
||||
return sorted( voice )
|
||||
|
||||
def get_voice_list(dir=get_voice_dir(), append_defaults=False):
|
||||
def get_voice_list(dir=get_voice_dir(), append_defaults=False, extensions=["wav", "mp3", "flac", "pth"]):
|
||||
defaults = [ "random", "microphone" ]
|
||||
os.makedirs(dir, exist_ok=True)
|
||||
#res = sorted([d for d in os.listdir(dir) if d not in defaults and os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ])
|
||||
|
|
@ -2993,7 +3085,7 @@ def get_voice_list(dir=get_voice_dir(), append_defaults=False):
|
|||
continue
|
||||
if len(os.listdir(os.path.join(dir, name))) == 0:
|
||||
continue
|
||||
files = get_voice( name, dir=dir )
|
||||
files = get_voice( name, dir=dir, extensions=extensions )
|
||||
|
||||
if len(files) > 0:
|
||||
res.append(name)
|
||||
|
|
@ -3001,7 +3093,7 @@ def get_voice_list(dir=get_voice_dir(), append_defaults=False):
|
|||
for subdir in os.listdir(f'{dir}/{name}'):
|
||||
if not os.path.isdir(f'{dir}/{name}/{subdir}'):
|
||||
continue
|
||||
files = get_voice( f'{name}/{subdir}', dir=dir )
|
||||
files = get_voice( f'{name}/{subdir}', dir=dir, extensions=extensions )
|
||||
if len(files) == 0:
|
||||
continue
|
||||
res.append(f'{name}/{subdir}')
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user