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added polyfill for loading autoregressive models in case mrq/tortoise-tts absolutely refuses to update

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mrq 2023-02-19 05:10:08 +00:00
parent e7d0cfaa82
commit f44239a85a
1 changed files with 25 additions and 2 deletions
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27
src/utils.py
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@ -543,7 +543,12 @@ def setup_tortoise(restart=False):
tts = None tts = None
print(f"Initializating TorToiSe... (using model: {args.autoregressive_model})") print(f"Initializating TorToiSe... (using model: {args.autoregressive_model})")
tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model) try:
tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model)
except Exception as e:
tts = TextToSpeech(minor_optimizations=not args.low_vram)
load_autoregressive_model(args.autoregressive_model)
get_model_path('dvae.pth') get_model_path('dvae.pth')
print("TorToiSe initialized, ready for generation.") print("TorToiSe initialized, ready for generation.")
return tts return tts
@ -826,7 +831,25 @@ def update_autoregressive_model(path_name):
raise Exception("TTS is uninitialized or still initializing...") raise Exception("TTS is uninitialized or still initializing...")
print(f"Loading model: {path_name}") print(f"Loading model: {path_name}")
tts.load_autoregressive_model(path_name)
if hasattr(tts, 'load_autoregressive_model') and tts.load_autoregressive_model(path_name):
tts.load_autoregressive_model(path_name)
# polyfill in case a user did NOT update the packages
else:
from tortoise.models.autoregressive import UnifiedVoice
tts.autoregressive_model_path = autoregressive_model_path if autoregressive_model_path and os.path.exists(autoregressive_model_path) else get_model_path('autoregressive.pth', tts.models_dir)
del tts.autoregressive
tts.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
model_dim=1024,
heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
train_solo_embeddings=False).cpu().eval()
tts.autoregressive.load_state_dict(torch.load(tts.autoregressive_model_path))
tts.autoregressive.post_init_gpt2_config(kv_cache=tts.use_kv_cache)
if tts.preloaded_tensors:
tts.autoregressive = tts.autoregressive.to(tts.device)
print(f"Loaded model: {tts.autoregressive_model_path}") print(f"Loaded model: {tts.autoregressive_model_path}")
args.autoregressive_model = path_name args.autoregressive_model = path_name