forked from mrq/ai-voice-cloning
made initialization faster if there's a lot of voice files (because glob fucking sucks), commiting changes buried on my training rig
This commit is contained in:
parent
91a0c495ff
commit
72a38ff2fc
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@ -1 +1 @@
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Subproject commit 5ff00bf3bfa97e2c8e9f166b920273f83ac9d8f0
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Subproject commit cbd3c95c42ac1da9772f61b9895954ee693075c9
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@ -8,3 +8,4 @@ voicefixer
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psutil
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phonemizer
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pydantic==1.10.11
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websockets
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302
src/utils.py
302
src/utils.py
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@ -45,7 +45,7 @@ from tortoise.utils.device import get_device_name, set_device_name, get_device_c
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MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
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WHISPER_MODELS = ["tiny", "base", "small", "medium", "large"]
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WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v1", "large-v2"]
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WHISPER_SPECIALIZED_MODELS = ["tiny.en", "base.en", "small.en", "medium.en"]
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WHISPER_BACKENDS = ["openai/whisper", "lightmare/whispercpp", "m-bain/whisperx"]
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VOCODERS = ['univnet', 'bigvgan_base_24khz_100band', 'bigvgan_24khz_100band']
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@ -61,12 +61,15 @@ RESAMPLERS = {}
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MIN_TRAINING_DURATION = 0.6
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MAX_TRAINING_DURATION = 11.6097505669
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MAX_TRAINING_CHAR_LENGTH = 200
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VALLE_ENABLED = False
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BARK_ENABLED = False
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VERBOSE_DEBUG = True
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import traceback
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try:
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from whisper.normalizers.english import EnglishTextNormalizer
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from whisper.normalizers.basic import BasicTextNormalizer
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@ -75,7 +78,7 @@ try:
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print("Whisper detected")
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except Exception as e:
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if VERBOSE_DEBUG:
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print("Error:", e)
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print(traceback.format_exc())
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pass
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try:
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@ -90,12 +93,14 @@ try:
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VALLE_ENABLED = True
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except Exception as e:
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if VERBOSE_DEBUG:
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print("Error:", e)
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print(traceback.format_exc())
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pass
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if VALLE_ENABLED:
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TTSES.append('vall-e')
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# torchaudio.set_audio_backend('soundfile')
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try:
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import bark
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from bark import text_to_semantic
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@ -109,35 +114,10 @@ try:
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BARK_ENABLED = True
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except Exception as e:
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if VERBOSE_DEBUG:
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print("Error:", e)
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print(traceback.format_exc())
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pass
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if BARK_ENABLED:
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try:
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from vocos import Vocos
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VOCOS_ENABLED = True
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print("Vocos detected")
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except Exception as e:
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if VERBOSE_DEBUG:
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print("Error:", e)
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VOCOS_ENABLED = False
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try:
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from hubert.hubert_manager import HuBERTManager
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from hubert.pre_kmeans_hubert import CustomHubert
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from hubert.customtokenizer import CustomTokenizer
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hubert_manager = HuBERTManager()
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hubert_manager.make_sure_hubert_installed()
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hubert_manager.make_sure_tokenizer_installed()
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HUBERT_ENABLED = True
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print("HuBERT detected")
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except Exception as e:
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if VERBOSE_DEBUG:
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print("Error:", e)
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HUBERT_ENABLED = False
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TTSES.append('bark')
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def semantic_to_audio_tokens(
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@ -181,7 +161,32 @@ if BARK_ENABLED:
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self.device = get_device_name()
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if VOCOS_ENABLED:
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try:
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from vocos import Vocos
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self.vocos_enabled = True
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print("Vocos detected")
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except Exception as e:
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if VERBOSE_DEBUG:
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print(traceback.format_exc())
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self.vocos_enabled = False
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try:
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from hubert.hubert_manager import HuBERTManager
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from hubert.pre_kmeans_hubert import CustomHubert
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from hubert.customtokenizer import CustomTokenizer
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hubert_manager = HuBERTManager()
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hubert_manager.make_sure_hubert_installed()
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hubert_manager.make_sure_tokenizer_installed()
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self.hubert_enabled = True
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print("HuBERT detected")
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except Exception as e:
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if VERBOSE_DEBUG:
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print(traceback.format_exc())
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self.hubert_enabled = False
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if self.vocos_enabled:
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self.vocos = Vocos.from_pretrained("charactr/vocos-encodec-24khz").to(self.device)
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def create_voice( self, voice ):
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@ -238,7 +243,7 @@ if BARK_ENABLED:
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# generate semantic tokens
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if HUBERT_ENABLED:
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if self.hubert_enabled:
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wav = wav.to(self.device)
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# Extract discrete codes from EnCodec
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@ -426,7 +431,7 @@ def generate_bark(**kwargs):
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idx_cache = {}
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for i, file in enumerate(os.listdir(outdir)):
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filename = os.path.basename(file)
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extension = os.path.splitext(filename)[1]
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extension = os.path.splitext(filename)[-1][1:]
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if extension != ".json" and extension != ".wav":
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continue
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match = re.findall(rf"^{cleanup_voice_name(voice)}_(\d+)(?:.+?)?{extension}$", filename)
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@ -672,18 +677,23 @@ def generate_valle(**kwargs):
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voice_cache = {}
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def fetch_voice( voice ):
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if voice in voice_cache:
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return voice_cache[voice]
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voice_dir = f'./training/{voice}/audio/'
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if not os.path.isdir(voice_dir):
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if not os.path.isdir(voice_dir) or len(os.listdir(voice_dir)) == 0:
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voice_dir = f'./voices/{voice}/'
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files = [ f'{voice_dir}/{d}' for d in os.listdir(voice_dir) if d[-4:] == ".wav" ]
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# return files
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return random.choice(files)
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voice_cache[voice] = random.choice(files)
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return voice_cache[voice]
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def get_settings( override=None ):
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settings = {
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'ar_temp': float(parameters['temperature']),
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'nar_temp': float(parameters['temperature']),
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'max_ar_samples': parameters['num_autoregressive_samples'],
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'max_ar_steps': parameters['num_autoregressive_samples'],
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}
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# could be better to just do a ternary on everything above, but i am not a professional
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@ -697,7 +707,7 @@ def generate_valle(**kwargs):
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continue
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settings[k] = override[k]
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settings['reference'] = fetch_voice(voice=selected_voice)
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settings['references'] = [ fetch_voice(voice=selected_voice) for _ in range(3) ]
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return settings
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if not parameters['delimiter']:
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@ -723,7 +733,7 @@ def generate_valle(**kwargs):
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idx_cache = {}
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for i, file in enumerate(os.listdir(outdir)):
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filename = os.path.basename(file)
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extension = os.path.splitext(filename)[1]
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extension = os.path.splitext(filename)[-1][1:]
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if extension != ".json" and extension != ".wav":
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continue
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match = re.findall(rf"^{voice}_(\d+)(?:.+?)?{extension}$", filename)
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@ -783,11 +793,14 @@ def generate_valle(**kwargs):
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except Exception as e:
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raise Exception("Prompt settings editing requested, but received invalid JSON")
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settings = get_settings( override=override )
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reference = settings['reference']
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settings.pop("reference")
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name = get_name(line=line, candidate=0)
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gen = tts.inference(cut_text, reference, **settings )
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settings = get_settings( override=override )
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references = settings['references']
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settings.pop("references")
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settings['out_path'] = f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav'
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gen = tts.inference(cut_text, references, **settings )
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run_time = time.time()-start_time
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print(f"Generating line took {run_time} seconds")
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@ -805,7 +818,7 @@ def generate_valle(**kwargs):
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# save here in case some error happens mid-batch
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#torchaudio.save(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', wav.cpu(), sr)
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soundfile.write(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', wav.cpu()[0,0], sr)
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#soundfile.write(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', wav.cpu()[0,0], sr)
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wav, sr = torchaudio.load(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav')
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audio_cache[name] = {
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@ -1085,7 +1098,7 @@ def generate_tortoise(**kwargs):
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idx_cache = {}
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for i, file in enumerate(os.listdir(outdir)):
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filename = os.path.basename(file)
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extension = os.path.splitext(filename)[1]
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extension = os.path.splitext(filename)[-1][1:]
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if extension != ".json" and extension != ".wav":
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continue
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match = re.findall(rf"^{voice}_(\d+)(?:.+?)?{extension}$", filename)
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@ -1605,30 +1618,18 @@ class TrainingState():
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if args.tts_backend == "vall-e":
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keys['lrs'] = [
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'ar.lr', 'nar.lr',
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'ar-half.lr', 'nar-half.lr',
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'ar-quarter.lr', 'nar-quarter.lr',
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]
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keys['losses'] = [
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'ar.loss', 'nar.loss', 'ar+nar.loss',
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'ar-half.loss', 'nar-half.loss', 'ar-half+nar-half.loss',
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'ar-quarter.loss', 'nar-quarter.loss', 'ar-quarter+nar-quarter.loss',
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# 'ar.loss', 'nar.loss', 'ar+nar.loss',
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'ar.loss.nll', 'nar.loss.nll',
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'ar-half.loss.nll', 'nar-half.loss.nll',
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'ar-quarter.loss.nll', 'nar-quarter.loss.nll',
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]
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keys['accuracies'] = [
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'ar.loss.acc', 'nar.loss.acc',
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'ar-half.loss.acc', 'nar-half.loss.acc',
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'ar-quarter.loss.acc', 'nar-quarter.loss.acc',
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'ar.stats.acc', 'nar.loss.acc',
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]
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keys['precisions'] = [
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'ar.loss.precision', 'nar.loss.precision',
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'ar-half.loss.precision', 'nar-half.loss.precision',
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'ar-quarter.loss.precision', 'nar-quarter.loss.precision',
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]
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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']
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keys['precisions'] = [ 'ar.loss.precision', 'nar.loss.precision', ]
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keys['grad_norms'] = ['ar.grad_norm', 'nar.grad_norm']
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for k in keys['lrs']:
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if k not in self.info:
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@ -1746,7 +1747,8 @@ class TrainingState():
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if args.tts_backend == "tortoise":
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logs = sorted([f'{self.training_dir}/finetune/{d}' for d in os.listdir(f'{self.training_dir}/finetune/') if d[-4:] == ".log" ])
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else:
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logs = sorted([f'{self.training_dir}/logs/{d}/log.txt' for d in os.listdir(f'{self.training_dir}/logs/') ])
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log_dir = "logs"
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logs = sorted([f'{self.training_dir}/{log_dir}/{d}/log.txt' for d in os.listdir(f'{self.training_dir}/{log_dir}/') ])
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if update:
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logs = [logs[-1]]
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@ -2220,6 +2222,8 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
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indir = f'./training/{voice}/'
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infile = f'{indir}/whisper.json'
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quantize_in_memory = args.tts_backend == "vall-e"
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os.makedirs(f'{indir}/audio/', exist_ok=True)
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TARGET_SAMPLE_RATE = 22050
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@ -2245,13 +2249,24 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
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continue
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results[basename] = result
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if not quantize_in_memory:
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waveform, sample_rate = torchaudio.load(file)
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# resample to the input rate, since it'll get resampled for training anyways
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# this should also "help" increase throughput a bit when filling the dataloaders
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waveform, sample_rate = resample(waveform, sample_rate, TARGET_SAMPLE_RATE)
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if waveform.shape[0] == 2:
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waveform = waveform[:1]
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torchaudio.save(f"{indir}/audio/{basename}", waveform, sample_rate, encoding="PCM_S", bits_per_sample=16)
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try:
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kwargs = {}
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if basename[-4:] == ".wav":
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kwargs['encoding'] = "PCM_S"
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kwargs['bits_per_sample'] = 16
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torchaudio.save(f"{indir}/audio/{basename}", waveform, sample_rate, **kwargs)
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except Exception as e:
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print(e)
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with open(infile, 'w', encoding="utf-8") as f:
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f.write(json.dumps(results, indent='\t'))
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@ -2317,6 +2332,9 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
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segments = 0
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for filename in results:
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path = f'./voices/{voice}/{filename}'
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extension = os.path.splitext(filename)[-1][1:]
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out_extension = extension # "wav"
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if not os.path.exists(path):
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path = f'./training/{voice}/{filename}'
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@ -2333,7 +2351,7 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
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duration = num_frames / sample_rate
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for segment in result['segments']:
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file = filename.replace(".wav", f"_{pad(segment['id'], 4)}.wav")
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file = filename.replace(f".{extension}", f"_{pad(segment['id'], 4)}.{out_extension}")
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sliced, error = slice_waveform( waveform, sample_rate, segment['start'] + start_offset, segment['end'] + end_offset, trim_silence )
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if error:
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@ -2341,12 +2359,17 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
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print(message)
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messages.append(message)
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continue
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sliced, _ = resample( sliced, sample_rate, TARGET_SAMPLE_RATE )
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# sliced, _ = resample( sliced, sample_rate, TARGET_SAMPLE_RATE )
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if waveform.shape[0] == 2:
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waveform = waveform[:1]
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torchaudio.save(f"{indir}/audio/{file}", sliced, TARGET_SAMPLE_RATE, encoding="PCM_S", bits_per_sample=16)
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kwargs = {}
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if file[-4:] == ".wav":
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kwargs['encoding'] = "PCM_S"
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kwargs['bits_per_sample'] = 16
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torchaudio.save(f"{indir}/audio/{file}", sliced, TARGET_SAMPLE_RATE, **kwargs)
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segments +=1
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@ -2462,18 +2485,32 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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errored = 0
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messages = []
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normalize = True
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normalize = False # True
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phonemize = should_phonemize()
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lines = { 'training': [], 'validation': [] }
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segments = {}
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quantize_in_memory = args.tts_backend == "vall-e"
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if args.tts_backend != "tortoise":
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text_length = 0
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audio_length = 0
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start_offset = -0.1
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end_offset = 0.1
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trim_silence = False
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TARGET_SAMPLE_RATE = 22050
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if args.tts_backend != "tortoise":
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TARGET_SAMPLE_RATE = 24000
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if tts:
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TARGET_SAMPLE_RATE = tts.input_sample_rate
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for filename in tqdm(results, desc="Parsing results"):
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use_segment = use_segments
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extension = os.path.splitext(filename)[-1][1:]
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out_extension = extension # "wav"
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result = results[filename]
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lang = result['language']
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language = LANGUAGES[lang] if lang in LANGUAGES else lang
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@ -2481,8 +2518,8 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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# check if unsegmented text exceeds 200 characters
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if not use_segment:
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if len(result['text']) > 200:
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message = f"Text length too long (200 < {len(result['text'])}), using segments: {filename}"
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if len(result['text']) > MAX_TRAINING_CHAR_LENGTH:
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message = f"Text length too long ({MAX_TRAINING_CHAR_LENGTH} < {len(result['text'])}), using segments: {filename}"
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print(message)
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messages.append(message)
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use_segment = True
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@ -2490,13 +2527,15 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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# check if unsegmented audio exceeds 11.6s
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if not use_segment:
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path = f'{indir}/audio/{filename}'
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if not os.path.exists(path):
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if not quantize_in_memory and not os.path.exists(path):
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messages.append(f"Missing source audio: {filename}")
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errored += 1
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continue
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metadata = torchaudio.info(path)
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duration = metadata.num_frames / metadata.sample_rate
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duration = 0
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for segment in result['segments']:
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duration = max(duration, result['segments'][segment]['end'])
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|
||||
if duration >= MAX_TRAINING_DURATION:
|
||||
message = f"Audio too large, using segments: {filename}"
|
||||
print(message)
|
||||
|
|
@ -2511,13 +2550,13 @@ 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 +2564,23 @@ 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 +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}')
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user