forked from mrq/ai-voice-cloning
Re-added missing joined variable
This commit is contained in:
apolygon
parent
0f31c34120
commit
7c9f55b1de
64
src/utils.py
64
src/utils.py
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@ -42,8 +42,6 @@ WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v2"]
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WHISPER_SPECIALIZED_MODELS = ["tiny.en", "base.en", "small.en", "medium.en"]
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WHISPER_BACKENDS = ["openai/whisper", "lightmare/whispercpp", "m-bain/whisperx"]
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VOCODERS = ['univnet', 'bigvgan_base_24khz_100band'] #, 'bigvgan_24khz_100band']
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EPOCH_SCHEDULE = [ 9, 18, 25, 33 ]
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args = None
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@ -986,9 +984,6 @@ def run_training(config_path, verbose=False, gpus=1, keep_x_past_datasets=0, pro
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if training_state and training_state.process:
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return "Training already in progress"
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# ensure we have the dvae.pth
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get_model_path('dvae.pth')
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# I don't know if this is still necessary, as it was bitching at me for not doing this, despite it being in a separate process
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torch.multiprocessing.freeze_support()
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@ -1198,6 +1193,10 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
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with open(f'{outdir}/whisper.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps(results, indent='\t'))
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joined = '\n'.join(transcription)
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with open(f'{outdir}/train.txt', 'w', encoding="utf-8") as f:
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f.write(joined)
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unload_whisper()
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return f"Processed dataset to: {outdir}\n{joined}"
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@ -1544,7 +1543,7 @@ def setup_args():
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'defer-tts-load': False,
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'device-override': None,
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'prune-nonfinal-outputs': True,
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'vocoder-model': VOCODERS[-1],
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'use-bigvgan-vocoder': True,
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'concurrency-count': 2,
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'autocalculate-voice-chunk-duration-size': 0,
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'output-sample-rate': 44100,
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@ -1581,7 +1580,7 @@ def setup_args():
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parser.add_argument("--force-cpu-for-conditioning-latents", default=default_arguments['force-cpu-for-conditioning-latents'], action='store_true', help="Forces computing conditional latents to be done on the CPU (if you constantyl OOM on low chunk counts)")
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parser.add_argument("--defer-tts-load", default=default_arguments['defer-tts-load'], action='store_true', help="Defers loading TTS model")
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parser.add_argument("--prune-nonfinal-outputs", default=default_arguments['prune-nonfinal-outputs'], action='store_true', help="Deletes non-final output files on completing a generation")
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parser.add_argument("--vocoder-model", default=default_arguments['vocoder-model'], action='store_true', help="Specifies with vocoder to use")
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parser.add_argument("--use-bigvgan-vocoder", default=default_arguments['use-bigvgan-vocoder'], action='store_true', help="Uses BigVGAN in place of the default vocoder")
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parser.add_argument("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch")
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parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets how many batches to use during the autoregressive samples pass")
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parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once")
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@ -1625,7 +1624,7 @@ def setup_args():
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return args
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def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, prune_nonfinal_outputs, device_override, sample_batch_size, concurrency_count, autocalculate_voice_chunk_duration_size, output_volume, autoregressive_model, vocoder_model, whisper_backend, whisper_model, training_default_halfp, training_default_bnb ):
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def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, prune_nonfinal_outputs, use_bigvgan_vocoder, device_override, sample_batch_size, concurrency_count, autocalculate_voice_chunk_duration_size, output_volume, autoregressive_model, whisper_backend, whisper_model, training_default_halfp, training_default_bnb ):
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global args
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args.listen = listen
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@ -1636,6 +1635,7 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v
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args.force_cpu_for_conditioning_latents = force_cpu_for_conditioning_latents
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args.defer_tts_load = defer_tts_load
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args.prune_nonfinal_outputs = prune_nonfinal_outputs
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args.use_bigvgan_vocoder = use_bigvgan_vocoder
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args.device_override = device_override
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args.sample_batch_size = sample_batch_size
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args.embed_output_metadata = embed_output_metadata
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@ -1648,7 +1648,6 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v
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args.output_volume = output_volume
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args.autoregressive_model = autoregressive_model
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args.vocoder_model = vocoder_model
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args.whisper_backend = whisper_backend
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args.whisper_model = whisper_model
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@ -1668,6 +1667,7 @@ def save_args_settings():
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'force-cpu-for-conditioning-latents': args.force_cpu_for_conditioning_latents,
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'defer-tts-load': args.defer_tts_load,
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'prune-nonfinal-outputs': args.prune_nonfinal_outputs,
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'use-bigvgan-vocoder': args.use_bigvgan_vocoder,
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'device-override': args.device_override,
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'sample-batch-size': args.sample_batch_size,
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'embed-output-metadata': args.embed_output_metadata,
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@ -1680,7 +1680,6 @@ def save_args_settings():
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'output-volume': args.output_volume,
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'autoregressive-model': args.autoregressive_model,
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'vocoder-model': args.vocoder_model,
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'whisper-backend': args.whisper_backend,
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'whisper-model': args.whisper_model,
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@ -1796,11 +1795,11 @@ def load_tts( restart=False, model=None ):
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if model:
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args.autoregressive_model = 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... (using model: {args.autoregressive_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=args.autoregressive_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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@ -1848,32 +1847,35 @@ def update_autoregressive_model(autoregressive_model_path):
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return
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print(f"Loading model: {autoregressive_model_path}")
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if hasattr(tts, 'load_autoregressive_model') and tts.load_autoregressive_model(autoregressive_model_path):
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tts.load_autoregressive_model(autoregressive_model_path)
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# polyfill in case a user did NOT update the packages
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# this shouldn't happen anymore, as I just clone mrq/tortoise-tts, and inject it into sys.path
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else:
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from tortoise.models.autoregressive import UnifiedVoice
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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)
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del tts.autoregressive
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tts.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
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model_dim=1024,
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heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
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train_solo_embeddings=False).cpu().eval()
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tts.autoregressive.load_state_dict(torch.load(tts.autoregressive_model_path))
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tts.autoregressive.post_init_gpt2_config(kv_cache=tts.use_kv_cache)
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if tts.preloaded_tensors:
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tts.autoregressive = tts.autoregressive.to(tts.device)
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if not hasattr(tts, 'autoregressive_model_hash'):
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tts.autoregressive_model_hash = hash_file(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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return autoregressive_model_path
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def update_vocoder_model(vocoder_model):
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args.vocoder_model = vocoder_model
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save_args_settings()
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print(f'Stored vocoder model to settings: {vocoder_model}')
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global tts
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if not tts:
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if tts_loading:
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raise Exception("TTS is still initializing...")
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return
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print(f"Loading model: {vocoder_model}")
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tts.load_vocoder_model(vocoder_model)
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print(f"Loaded model: {tts.vocoder_model}")
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do_gc()
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return vocoder_model
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def load_voicefixer(restart=False):
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global voicefixer
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