cleaned up some model loading logic, added 'auto' mode for AR model (deduced by current voice)
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3899f9b4e3
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d7a5ad9fd9
107
src/utils.py
107
src/utils.py
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@ -54,6 +54,8 @@ voicefixer = None
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whisper_model = None
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training_state = None
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current_voice = None
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def generate(
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text,
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delimiter,
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@ -117,10 +119,7 @@ def generate(
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else:
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progress(0, desc=f"Loading voice: {voice}")
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# nasty check for users that, for whatever reason, updated the web UI but not mrq/tortoise-tts
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if hasattr(tts, 'autoregressive_model_hash'):
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voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
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else:
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voice_samples, conditioning_latents = load_voice(voice)
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voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
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if voice_samples and len(voice_samples) > 0:
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conditioning_latents = compute_latents(voice=voice, voice_samples=voice_samples, voice_latents_chunks=voice_latents_chunks)
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@ -146,6 +145,10 @@ def generate(
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print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents.")
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cvvp_weight = 0
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autoregressive_model = args.autoregressive_model
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if autoregressive_model == "auto":
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autoregressive_model = deduce_autoregressive_model(voice)
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def get_settings( override=None ):
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settings = {
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'temperature': float(temperature),
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@ -172,7 +175,7 @@ def generate(
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'half_p': "Half Precision" in experimental_checkboxes,
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'cond_free': "Conditioning-Free" in experimental_checkboxes,
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'cvvp_amount': cvvp_weight,
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'autoregressive_model': args.autoregressive_model,
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'autoregressive_model': autoregressive_model,
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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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@ -180,18 +183,10 @@ def generate(
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if 'voice' in override:
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voice = override['voice']
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if "autoregressive_model" in override and override["autoregressive_model"] == "auto":
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dir = f'./training/{voice}-finetune/models/'
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if os.path.exists(f'./training/finetunes/{voice}.pth'):
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override["autoregressive_model"] = f'./training/finetunes/{voice}.pth'
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elif os.path.isdir(dir):
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counts = sorted([ int(d[:-8]) for d in os.listdir(dir) if d[-8:] == "_gpt.pth" ])
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names = [ f'./{dir}/{d}_gpt.pth' for d in counts ]
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override["autoregressive_model"] = names[-1]
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else:
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override["autoregressive_model"] = None
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if "autoregressive_model" in override:
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if override["autoregressive_model"] == "auto":
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override["autoregressive_model"] = deduce_autoregressive_model(voice)
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# necessary to ensure the right model gets loaded for the latents
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tts.load_autoregressive_model( override["autoregressive_model"] )
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fetched = fetch_voice(voice)
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@ -204,8 +199,7 @@ def generate(
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continue
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settings[k] = override[k]
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if hasattr(tts, 'autoregressive_model_path') and tts.autoregressive_model_path != settings["autoregressive_model"]:
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tts.load_autoregressive_model( settings["autoregressive_model"] )
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tts.load_autoregressive_model( settings["autoregressive_model"] )
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# clamp it down for the insane users who want this
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# it would be wiser to enforce the sample size to the batch size, but this is what the user wants
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@ -302,7 +296,7 @@ def generate(
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'datetime': datetime.now().isoformat(),
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'model': tts.autoregressive_model_path,
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'model_hash': tts.autoregressive_model_hash if hasattr(tts, 'autoregressive_model_hash') else None,
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'model_hash': tts.autoregressive_model_hash
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}
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if settings is not None:
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@ -331,7 +325,7 @@ def generate(
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else:
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if settings and "model_hash" in settings:
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latents_path = f'{get_voice_dir()}/{voice}/cond_latents_{settings["model_hash"][:8]}.pth'
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elif hasattr(tts, "autoregressive_model_hash"):
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else:
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latents_path = f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth'
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if latents_path and os.path.exists(latents_path):
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@ -387,7 +381,7 @@ def generate(
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used_settings['time'] = run_time
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used_settings['datetime'] = datetime.now().isoformat(),
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used_settings['model'] = tts.autoregressive_model_path
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used_settings['model_hash'] = tts.autoregressive_model_hash if hasattr(tts, 'autoregressive_model_hash') else None
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used_settings['model_hash'] = tts.autoregressive_model_hash
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audio_cache[name] = {
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'audio': audio,
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@ -540,6 +534,9 @@ def hash_file(path, algo="md5", buffer_size=0):
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return "{0}".format(hash.hexdigest())
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def update_baseline_for_latents_chunks( voice ):
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global current_voice
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current_voice = voice
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path = f'{get_voice_dir()}/{voice}/'
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if not os.path.isdir(path):
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return 1
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@ -583,6 +580,9 @@ def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, prog
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if hasattr(tts, "loading") and tts.loading:
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raise Exception("TTS is still initializing...")
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if args.autoregressive_model == "auto":
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tts.load_autoregressive_model(deduce_autoregressive_model(voice))
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if voice:
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load_from_dataset = voice_latents_chunks == 0
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@ -620,10 +620,7 @@ def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, prog
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if len(conditioning_latents) == 4:
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conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
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if hasattr(tts, 'autoregressive_model_hash'):
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
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else:
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
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return conditioning_latents
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@ -1460,6 +1457,9 @@ def get_autoregressive_models(dir="./models/finetunes/", prefixed=False):
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models = sorted([ int(d[:-8]) for d in os.listdir(f'./training/{training}/models/') if d[-8:] == "_gpt.pth" ])
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found = found + [ f'./training/{training}/models/{d}_gpt.pth' for d in models ]
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if len(found) > 0 or len(additionals) > 0:
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base = ["auto"] + base
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res = base + additionals + found
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if prefixed:
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@ -1815,28 +1815,29 @@ def version_check_tts( min_version ):
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return True
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return False
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def load_tts( restart=False, model=None ):
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def load_tts( restart=False, autoregressive_model=None ):
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global args
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global tts
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if restart:
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unload_tts()
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if autoregressive_model:
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args.autoregressive_model = autoregressive_model
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else:
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autoregressive_model = args.autoregressive_model
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if model:
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args.autoregressive_model = model
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if autoregressive_model == "auto":
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autoregressive_model = deduce_autoregressive_model()
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print(f"Loading TorToiSe... (AR: {args.autoregressive_model}, vocoder: {args.vocoder_model})")
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print(f"Loading TorToiSe... (AR: {autoregressive_model}, vocoder: {args.vocoder_model})")
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tts_loading = True
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try:
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tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model, vocoder_model=args.vocoder_model)
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tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=autoregressive_model, vocoder_model=args.vocoder_model)
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except Exception as e:
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tts = TextToSpeech(minor_optimizations=not args.low_vram)
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load_autoregressive_model(args.autoregressive_model)
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if not hasattr(tts, 'autoregressive_model_hash'):
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tts.autoregressive_model_hash = hash_file(tts.autoregressive_model_path)
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load_autoregressive_model(autoregressive_model)
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tts_loading = False
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@ -1858,6 +1859,37 @@ def unload_tts():
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def reload_tts( model=None ):
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load_tts( restart=True, model=model )
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def get_current_voice():
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global current_voice
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if current_voice:
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return current_voice
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settings, _ = read_generate_settings("./config/generate.json", read_latents=False)
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if settings and "voice" in settings['voice']:
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return settings["voice"]
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return None
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def deduce_autoregressive_model(voice=None):
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if not voice:
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voice = get_current_voice()
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if voice:
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dir = f'./training/{voice}-finetune/models/'
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if os.path.exists(f'./training/finetunes/{voice}.pth'):
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return f'./training/finetunes/{voice}.pth'
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if os.path.isdir(dir):
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counts = sorted([ int(d[:-8]) for d in os.listdir(dir) if d[-8:] == "_gpt.pth" ])
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names = [ f'{dir}/{d}_gpt.pth' for d in counts ]
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return names[-1]
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if args.autoregressive_model != "auto":
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return args.autoregressive_model
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return get_model_path('autoregressive.pth')
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def update_autoregressive_model(autoregressive_model_path):
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match = re.findall(r'^\[[a-fA-F0-9]{8}\] (.+?)$', autoregressive_model_path)
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if match:
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@ -1880,10 +1912,13 @@ def update_autoregressive_model(autoregressive_model_path):
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if hasattr(tts, "loading") and tts.loading:
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raise Exception("TTS is still initializing...")
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if autoregressive_model_path == "auto":
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autoregressive_model_path = deduce_autoregressive_model()
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if autoregressive_model_path == tts.autoregressive_model_path:
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return
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print(f"Loading model: {autoregressive_model_path}")
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tts.load_autoregressive_model(autoregressive_model_path)
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print(f"Loaded model: {tts.autoregressive_model_path}")
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do_gc()
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@ -1 +1 @@
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Subproject commit e2db36af602297501132f7f68331755f5904825a
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Subproject commit 26133c20314b77155e77be804b43909dab9809d6
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