mrq/tortoise-tts
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added loading vocoders on the fly

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mrq 2023-03-07 02:44:09 +00:00
parent 7b2aa51abc
commit e2db36af60
1 changed files with 42 additions and 18 deletions
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tortoise/api.py
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@ -44,6 +44,7 @@ MODELS = {
'rlg_auto.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_auto.pth', 'rlg_auto.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_auto.pth',
'rlg_diffuser.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_diffuser.pth', 'rlg_diffuser.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_diffuser.pth',
'bigvgan_base_24khz_100band.pth': 'https://huggingface.co/ecker/tortoise-tts-models/resolve/main/models/bigvgan_base_24khz_100band.pth', 'bigvgan_base_24khz_100band.pth': 'https://huggingface.co/ecker/tortoise-tts-models/resolve/main/models/bigvgan_base_24khz_100band.pth',
#'bigvgan_24khz_100band.pth': 'https://huggingface.co/ecker/tortoise-tts-models/resolve/main/models/bigvgan_24khz_100band.pth',
} }
def hash_file(path, algo="md5", buffer_size=0): def hash_file(path, algo="md5", buffer_size=0):
@ -241,7 +242,7 @@ class TextToSpeech:
Main entry point into Tortoise. Main entry point into Tortoise.
""" """
def __init__(self, autoregressive_batch_size=None, models_dir=MODELS_DIR, enable_redaction=True, device=None, minor_optimizations=True, input_sample_rate=22050, output_sample_rate=24000, autoregressive_model_path=None, use_bigvgan=True): def __init__(self, autoregressive_batch_size=None, models_dir=MODELS_DIR, enable_redaction=True, device=None, minor_optimizations=True, input_sample_rate=22050, output_sample_rate=24000, autoregressive_model_path=None, vocoder_model=None):
""" """
Constructor Constructor
:param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing :param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing
@ -253,6 +254,7 @@ class TextToSpeech:
Default is true. Default is true.
:param device: Device to use when running the model. If omitted, the device will be automatically chosen. :param device: Device to use when running the model. If omitted, the device will be automatically chosen.
""" """
self.loading = True
if device is None: if device is None:
device = get_device(verbose=True) device = get_device(verbose=True)
@ -278,19 +280,13 @@ class TextToSpeech:
self.tokenizer = VoiceBpeTokenizer() self.tokenizer = VoiceBpeTokenizer()
self.autoregressive_model_path = autoregressive_model_path if autoregressive_model_path and os.path.exists(autoregressive_model_path) else get_model_path('autoregressive.pth', models_dir) self.autoregressive_model_path = autoregressive_model_path if autoregressive_model_path and os.path.exists(autoregressive_model_path) else get_model_path('autoregressive.pth', models_dir)
self.autoregressive_model_hash = hash_file(self.autoregressive_model_path)
if os.path.exists(f'{models_dir}/autoregressive.ptt'): if os.path.exists(f'{models_dir}/autoregressive.ptt'):
# Assume this is a traced directory. # Assume this is a traced directory.
self.autoregressive = torch.jit.load(f'{models_dir}/autoregressive.ptt') self.autoregressive = torch.jit.load(f'{models_dir}/autoregressive.ptt')
self.diffusion = torch.jit.load(f'{models_dir}/diffusion_decoder.ptt') self.diffusion = torch.jit.load(f'{models_dir}/diffusion_decoder.ptt')
else: else:
self.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30, self.load_autoregressive_model(self.autoregressive_model_path)
model_dim=1024,
heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
train_solo_embeddings=False).cpu().eval()
self.autoregressive.load_state_dict(torch.load(self.autoregressive_model_path))
self.autoregressive.post_init_gpt2_config(kv_cache=self.use_kv_cache)
self.diffusion = DiffusionTts(model_channels=1024, num_layers=10, in_channels=100, out_channels=200, self.diffusion = DiffusionTts(model_channels=1024, num_layers=10, in_channels=100, out_channels=200,
in_latent_channels=1024, in_tokens=8193, dropout=0, use_fp16=False, num_heads=16, in_latent_channels=1024, in_tokens=8193, dropout=0, use_fp16=False, num_heads=16,
@ -305,14 +301,8 @@ class TextToSpeech:
self.clvp.load_state_dict(torch.load(get_model_path('clvp2.pth', models_dir))) self.clvp.load_state_dict(torch.load(get_model_path('clvp2.pth', models_dir)))
self.cvvp = None # CVVP model is only loaded if used. self.cvvp = None # CVVP model is only loaded if used.
if use_bigvgan: self.vocoder_model = vocoder_model
# credit to https://github.com/deviandice / https://git.ecker.tech/mrq/ai-voice-cloning/issues/52 self.load_vocoder_model(self.vocoder_model)
self.vocoder = BigVGAN().cpu()
self.vocoder.load_state_dict(torch.load(get_model_path('bigvgan_base_24khz_100band.pth', models_dir), map_location=torch.device('cpu'))['generator'])
else:
self.vocoder = UnivNetGenerator().cpu()
self.vocoder.load_state_dict(torch.load(get_model_path('vocoder.pth', models_dir), map_location=torch.device('cpu'))['model_g'])
self.vocoder.eval(inference=True)
# Random latent generators (RLGs) are loaded lazily. # Random latent generators (RLGs) are loaded lazily.
self.rlg_auto = None self.rlg_auto = None
@ -323,13 +313,18 @@ class TextToSpeech:
self.diffusion = self.diffusion.to(self.device) self.diffusion = self.diffusion.to(self.device)
self.clvp = self.clvp.to(self.device) self.clvp = self.clvp.to(self.device)
self.vocoder = self.vocoder.to(self.device) self.vocoder = self.vocoder.to(self.device)
self.loading = False
def load_autoregressive_model(self, autoregressive_model_path): def load_autoregressive_model(self, autoregressive_model_path):
self.loading = True
previous_path = self.autoregressive_model_path previous_path = self.autoregressive_model_path
self.autoregressive_model_path = autoregressive_model_path if autoregressive_model_path and os.path.exists(autoregressive_model_path) else get_model_path('autoregressive.pth', self.models_dir) self.autoregressive_model_path = autoregressive_model_path if autoregressive_model_path and os.path.exists(autoregressive_model_path) else get_model_path('autoregressive.pth', self.models_dir)
self.autoregressive_model_hash = hash_file(self.autoregressive_model_path) self.autoregressive_model_hash = hash_file(self.autoregressive_model_path)
if hasattr(self, 'autoregressive'):
del self.autoregressive del self.autoregressive
self.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30, self.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
model_dim=1024, model_dim=1024,
heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False, heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
@ -339,8 +334,37 @@ class TextToSpeech:
if self.preloaded_tensors: if self.preloaded_tensors:
self.autoregressive = self.autoregressive.to(self.device) self.autoregressive = self.autoregressive.to(self.device)
self.loading = False
return previous_path != self.autoregressive_model_path def load_vocoder_model(self, vocoder_model):
self.loading = True
if hasattr(self, 'vocoder'):
del self.vocoder
print(vocoder_model)
if vocoder_model is None:
vocoder_model = 'bigvgan_24khz_100band'
if 'bigvgan' in vocoder_model:
# credit to https://github.com/deviandice / https://git.ecker.tech/mrq/ai-voice-cloning/issues/52
vocoder_key = 'generator'
self.vocoder_model_path = 'bigvgan_24khz_100band.pth'
if f'{vocoder_model}.pth' in MODELS:
self.vocoder_model_path = f'{vocoder_model}.pth'
self.vocoder = BigVGAN().cpu()
#elif vocoder_model == "univnet":
else:
vocoder_key = 'model_g'
self.vocoder_model_path = 'vocoder.pth'
self.vocoder = UnivNetGenerator().cpu()
print(vocoder_model, vocoder_key, self.vocoder_model_path)
self.vocoder.load_state_dict(torch.load(get_model_path(self.vocoder_model_path, self.models_dir), map_location=torch.device('cpu'))[vocoder_key])
self.vocoder.eval(inference=True)
if self.preloaded_tensors:
self.vocoder = self.vocoder.to(self.device)
self.loading = False
def load_cvvp(self): def load_cvvp(self):
"""Load CVVP model.""" """Load CVVP model."""