added storing the loaded model's hash to the TTS object instead of relying on jerryrig injecting it (although I still have to for the weirdos who refuse to update the right way), added a parameter when loading voices to load a latent tagged with a model's hash so latents are per-model now
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@ -42,6 +42,32 @@ MODELS = {
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'rlg_diffuser.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_diffuser.pth',
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}
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def hash_file(path, algo="md5", buffer_size=0):
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import hashlib
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hash = None
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if algo == "md5":
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hash = hashlib.md5()
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elif algo == "sha1":
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hash = hashlib.sha1()
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else:
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raise Exception(f'Unknown hash algorithm specified: {algo}')
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if not os.path.exists(path):
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raise Exception(f'Path not found: {path}')
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with open(path, 'rb') as f:
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if buffer_size > 0:
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while True:
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data = f.read(buffer_size)
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if not data:
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break
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hash.update(data)
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else:
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hash.update(f.read())
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return "{0}".format(hash.hexdigest())
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def check_for_kill_signal():
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global STOP_SIGNAL
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if STOP_SIGNAL:
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@ -221,16 +247,6 @@ class TextToSpeech:
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if device is None:
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device = get_device(verbose=True)
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try:
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import tortoise.utils.torch_intermediary as ml
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if ml.OVERRIDE_ADAM:
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print("Using BitsAndBytes ADAMW optimizations")
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else:
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print("NOT using BitsAndBytes ADAMW optimizations")
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except Exception as e:
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print(e)
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pass
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self.input_sample_rate = input_sample_rate
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self.output_sample_rate = output_sample_rate
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self.minor_optimizations = minor_optimizations
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@ -252,6 +268,7 @@ class TextToSpeech:
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self.tokenizer = VoiceBpeTokenizer()
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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)
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self.autoregressive_model_hash = hash_file(self.autoregressive_model_path)
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if os.path.exists(f'{models_dir}/autoregressive.ptt'):
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# Assume this is a traced directory.
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@ -295,6 +312,7 @@ class TextToSpeech:
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def load_autoregressive_model(self, autoregressive_model_path):
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previous_path = self.autoregressive_model_path
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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)
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self.autoregressive_model_hash = hash_file(self.autoregressive_model_path)
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del self.autoregressive
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self.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
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@ -306,6 +324,7 @@ class TextToSpeech:
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if self.preloaded_tensors:
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self.autoregressive = self.autoregressive.to(self.device)
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return previous_path != self.autoregressive_model_path
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def load_cvvp(self):
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@ -91,25 +91,28 @@ def get_voices(extra_voice_dirs=[], load_latents=True):
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return voices
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def load_voice(voice, extra_voice_dirs=[], load_latents=True, sample_rate=22050, device='cpu'):
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def load_voice(voice, extra_voice_dirs=[], load_latents=True, sample_rate=22050, device='cpu', model_hash=None):
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if voice == 'random':
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return None, None
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voices = get_voices(extra_voice_dirs=extra_voice_dirs, load_latents=load_latents)
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paths = voices[voice]
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paths = voices[voice]
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mtime = 0
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voices = []
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latent = None
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for file in paths:
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if file[-16:] == "cond_latents.pth":
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latent = file
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elif file[-4:] == ".pth":
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{}
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# noop
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voices = []
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for path in paths:
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filename = os.path.basename(path)
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if filename[-4:] == ".pth" and filename[:12] == "cond_latents":
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if not model_hash and filename == "cond_latents.pth":
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latent = path
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elif model_hash and filename == f"cond_latents_{model_hash[:8]}.pth":
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latent = path
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else:
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voices.append(file)
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mtime = max(mtime, os.path.getmtime(file))
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voices.append(path)
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mtime = max(mtime, os.path.getmtime(path))
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if load_latents and latent is not None:
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if os.path.getmtime(latent) > mtime:
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