From 72a38ff2fc6aeb8b8184bb8e8103497a37ce151d Mon Sep 17 00:00:00 2001
From: mrq <mrq@ecker.tech>
Date: Mon, 21 Aug 2023 03:31:49 +0000
Subject: [PATCH] made initialization faster if there's a lot of voice files
(because glob fucking sucks), commiting changes buried on my training rig
---
modules/tortoise-tts | 2 +-
requirements.txt | 3 +-
src/utils.py | 314 +++++++++++++++++++++++++++----------------
3 files changed, 201 insertions(+), 118 deletions(-)
diff --git a/modules/tortoise-tts b/modules/tortoise-tts
index 5ff00bf..cbd3c95 160000
--- a/modules/tortoise-tts
+++ b/modules/tortoise-tts
@@ -1 +1 @@
-Subproject commit 5ff00bf3bfa97e2c8e9f166b920273f83ac9d8f0
+Subproject commit cbd3c95c42ac1da9772f61b9895954ee693075c9
diff --git a/requirements.txt b/requirements.txt
index e1794a4..fcb0746 100755
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,4 +7,5 @@ music-tag
voicefixer
psutil
phonemizer
-pydantic==1.10.11
\ No newline at end of file
+pydantic==1.10.11
+websockets
\ No newline at end of file
diff --git a/src/utils.py b/src/utils.py
index 759e054..a79c6ce 100755
--- a/src/utils.py
+++ b/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]]
@@ -2219,6 +2221,8 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
files = get_voice(voice, load_latents=False)
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)
@@ -2245,13 +2249,24 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
continue
results[basename] = result
- 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)
+
+ 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]
+
+ 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 +2359,17 @@ 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 )
+ # 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 +2485,32 @@ 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 +2518,8 @@ 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 +2527,15 @@ 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)
@@ -2511,19 +2550,36 @@ 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}")
message = slice_dataset( voice, results=tmp )
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] = {
@@ -2533,13 +2589,18 @@ 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 +2608,27 @@ 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 +2639,20 @@ 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 +2663,8 @@ 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 +2674,46 @@ 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 +2722,7 @@ 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)
@@ -2980,7 +3062,7 @@ def get_voice( name, dir=get_voice_dir(), load_latents=True ):
voice = voice + list(glob(f'{subj}/*.pth'))
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 +3075,7 @@ 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 +3083,7 @@ 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}')