moved (actually not working) setting to use BigVGAN to a dropdown to select between vocoders (for when slotting in future ones), and ability to load a new vocoder while TTS is loaded
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59
src/utils.py
59
src/utils.py
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@ -42,6 +42,8 @@ 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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@ -1539,7 +1541,7 @@ 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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'use-bigvgan-vocoder': True,
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'vocoder-model': VOCODERS[-1],
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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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@ -1576,7 +1578,7 @@ 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("--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("--vocoder-model", default=default_arguments['vocoder-model'], action='store_true', help="Specifies with vocoder to use")
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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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@ -1620,7 +1622,7 @@ 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, 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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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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global args
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args.listen = listen
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@ -1631,7 +1633,6 @@ 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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@ -1644,6 +1645,7 @@ 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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@ -1663,7 +1665,6 @@ 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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@ -1676,6 +1677,7 @@ 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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@ -1791,11 +1793,11 @@ 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... (using model: {args.autoregressive_model})")
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print(f"Loading TorToiSe... (AR: {args.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)
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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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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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@ -1843,35 +1845,32 @@ 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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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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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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10
src/webui.py
10
src/webui.py
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@ -577,7 +577,6 @@ def setup_gradio():
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gr.Checkbox(label="Force CPU for Conditioning Latents", value=args.force_cpu_for_conditioning_latents),
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gr.Checkbox(label="Do Not Load TTS On Startup", value=args.defer_tts_load),
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gr.Checkbox(label="Delete Non-Final Output", value=args.prune_nonfinal_outputs),
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gr.Checkbox(label="Use BigVGAN Vocoder", value=args.use_bigvgan_vocoder),
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gr.Textbox(label="Device Override", value=args.device_override),
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]
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with gr.Column():
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@ -590,10 +589,11 @@ def setup_gradio():
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autoregressive_model_dropdown = gr.Dropdown(choices=autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else autoregressive_models[0])
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vocoder_models = gr.Dropdown(VOCODERS, label="Vocoder", value=args.vocoder_model if args.vocoder_model else VOCODERS[-1])
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whisper_backend = gr.Dropdown(WHISPER_BACKENDS, label="Whisper Backends", value=args.whisper_backend)
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whisper_model_dropdown = gr.Dropdown(WHISPER_MODELS, label="Whisper Model", value=args.whisper_model)
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exec_inputs = exec_inputs + [ autoregressive_model_dropdown, whisper_backend, whisper_model_dropdown, training_halfp, training_bnb ]
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exec_inputs = exec_inputs + [ autoregressive_model_dropdown, vocoder_models, whisper_backend, whisper_model_dropdown, training_halfp, training_bnb ]
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with gr.Row():
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autoregressive_models_update_button = gr.Button(value="Refresh Model List")
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@ -626,6 +626,12 @@ def setup_gradio():
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outputs=None
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)
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vocoder_models.change(
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fn=update_vocoder_model,
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inputs=vocoder_models,
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outputs=None
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)
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input_settings = [
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text,
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delimiter,
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@ -1 +1 @@
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Subproject commit 6fcd8c604f066e4e346da522bd14e6670395025f
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Subproject commit e2db36af602297501132f7f68331755f5904825a
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