HarkonCollider/tortoise-tts
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do not reload AR/vocoder if already loaded

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mrq 2023-03-07 04:33:49 +00:00
parent e2db36af60
commit 26133c2031
1 changed files with 20 additions and 10 deletions
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30
tortoise/api.py
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@ -279,14 +279,16 @@ class TextToSpeech:
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)
if os.path.exists(f'{models_dir}/autoregressive.ptt'):
# Assume this is a traced directory.
self.autoregressive = torch.jit.load(f'{models_dir}/autoregressive.ptt')
self.diffusion = torch.jit.load(f'{models_dir}/diffusion_decoder.ptt')
else:
self.load_autoregressive_model(self.autoregressive_model_path)
if not autoregressive_model_path or not os.path.exists(autoregressive_model_path):
autoregressive_model_path = get_model_path('autoregressive.pth', models_dir)
self.load_autoregressive_model(autoregressive_model_path)
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,
@ -316,11 +318,14 @@ class TextToSpeech:
self.loading = False
def load_autoregressive_model(self, autoregressive_model_path):
if hasattr(self,"autoregressive_model_path") and self.autoregressive_model_path == autoregressive_model_path:
return
self.loading = True
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_hash = hash_file(self.autoregressive_model_path)
print(f"Loading autoregressive model: {self.autoregressive_model_path}")
if hasattr(self, 'autoregressive'):
del self.autoregressive
@ -335,9 +340,14 @@ class TextToSpeech:
self.autoregressive = self.autoregressive.to(self.device)
self.loading = False
print(f"Loaded autoregressive model")
def load_vocoder_model(self, vocoder_model):
if hasattr(self,"vocoder_model_path") and self.vocoder_model_path == vocoder_model:
return
self.loading = True
if hasattr(self, 'vocoder'):
del self.vocoder
@ -357,14 +367,15 @@ class TextToSpeech:
vocoder_key = 'model_g'
self.vocoder_model_path = 'vocoder.pth'
self.vocoder = UnivNetGenerator().cpu()
print(vocoder_model, vocoder_key, self.vocoder_model_path)
print(f"Loading vocoder model: {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
print(f"Loaded vocoder model")
def load_cvvp(self):
"""Load CVVP model."""
@ -427,11 +438,10 @@ class TextToSpeech:
if slices == 0:
slices = 1
else:
if max_chunk_size is not None and chunk_size > max_chunk_size:
slices = 1
while int(chunk_size / slices) > max_chunk_size:
slices = slices + 1
elif max_chunk_size is not None and chunk_size > max_chunk_size:
slices = 1
while int(chunk_size / slices) > max_chunk_size:
slices = slices + 1
chunks = torch.chunk(concat, slices, dim=1)
chunk_size = chunks[0].shape[-1]