diff --git a/src/utils.py b/src/utils.py index 6fe3ac5..efa71ed 100755 --- a/src/utils.py +++ b/src/utils.py @@ -42,6 +42,8 @@ WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v2"] WHISPER_SPECIALIZED_MODELS = ["tiny.en", "base.en", "small.en", "medium.en"] WHISPER_BACKENDS = ["openai/whisper", "lightmare/whispercpp", "m-bain/whisperx"] +VOCODERS = ['univnet', 'bigvgan_base_24khz_100band'] #, 'bigvgan_24khz_100band'] + EPOCH_SCHEDULE = [ 9, 18, 25, 33 ] args = None @@ -1539,7 +1541,7 @@ def setup_args(): 'defer-tts-load': False, 'device-override': None, 'prune-nonfinal-outputs': True, - 'use-bigvgan-vocoder': True, + 'vocoder-model': VOCODERS[-1], 'concurrency-count': 2, 'autocalculate-voice-chunk-duration-size': 0, 'output-sample-rate': 44100, @@ -1576,7 +1578,7 @@ def setup_args(): 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)") parser.add_argument("--defer-tts-load", default=default_arguments['defer-tts-load'], action='store_true', help="Defers loading TTS model") 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") - parser.add_argument("--use-bigvgan-vocoder", default=default_arguments['use-bigvgan-vocoder'], action='store_true', help="Uses BigVGAN in place of the default vocoder") + parser.add_argument("--vocoder-model", default=default_arguments['vocoder-model'], action='store_true', help="Specifies with vocoder to use") parser.add_argument("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch") 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") parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once") @@ -1620,7 +1622,7 @@ def setup_args(): return args -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 ): +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 ): global args args.listen = listen @@ -1631,7 +1633,6 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v args.force_cpu_for_conditioning_latents = force_cpu_for_conditioning_latents args.defer_tts_load = defer_tts_load args.prune_nonfinal_outputs = prune_nonfinal_outputs - args.use_bigvgan_vocoder = use_bigvgan_vocoder args.device_override = device_override args.sample_batch_size = sample_batch_size args.embed_output_metadata = embed_output_metadata @@ -1644,6 +1645,7 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v args.output_volume = output_volume args.autoregressive_model = autoregressive_model + args.vocoder_model = vocoder_model args.whisper_backend = whisper_backend args.whisper_model = whisper_model @@ -1663,7 +1665,6 @@ def save_args_settings(): 'force-cpu-for-conditioning-latents': args.force_cpu_for_conditioning_latents, 'defer-tts-load': args.defer_tts_load, 'prune-nonfinal-outputs': args.prune_nonfinal_outputs, - 'use-bigvgan-vocoder': args.use_bigvgan_vocoder, 'device-override': args.device_override, 'sample-batch-size': args.sample_batch_size, 'embed-output-metadata': args.embed_output_metadata, @@ -1676,6 +1677,7 @@ def save_args_settings(): 'output-volume': args.output_volume, 'autoregressive-model': args.autoregressive_model, + 'vocoder-model': args.vocoder_model, 'whisper-backend': args.whisper_backend, 'whisper-model': args.whisper_model, @@ -1791,11 +1793,11 @@ def load_tts( restart=False, model=None ): if model: args.autoregressive_model = model - print(f"Loading TorToiSe... (using model: {args.autoregressive_model})") + print(f"Loading TorToiSe... (AR: {args.autoregressive_model}, vocoder: {args.vocoder_model})") tts_loading = True try: - tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model) + tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model, vocoder_model=args.vocoder_model) except Exception as e: tts = TextToSpeech(minor_optimizations=not args.low_vram) load_autoregressive_model(args.autoregressive_model) @@ -1843,34 +1845,31 @@ def update_autoregressive_model(autoregressive_model_path): return print(f"Loading model: {autoregressive_model_path}") + tts.load_autoregressive_model(autoregressive_model_path) + print(f"Loaded model: {tts.autoregressive_model_path}") - if hasattr(tts, 'load_autoregressive_model') and tts.load_autoregressive_model(autoregressive_model_path): - tts.load_autoregressive_model(autoregressive_model_path) - # polyfill in case a user did NOT update the packages - # this shouldn't happen anymore, as I just clone mrq/tortoise-tts, and inject it into sys.path - 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) + do_gc() + + return autoregressive_model_path - 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) +def update_vocoder_model(vocoder_model): + args.vocoder_model = vocoder_model + save_args_settings() + print(f'Stored vocoder model to settings: {vocoder_model}') - if not hasattr(tts, 'autoregressive_model_hash'): - tts.autoregressive_model_hash = hash_file(autoregressive_model_path) + global tts + if not tts: + if tts_loading: + raise Exception("TTS is still initializing...") + return - print(f"Loaded model: {tts.autoregressive_model_path}") + print(f"Loading model: {vocoder_model}") + tts.load_vocoder_model(vocoder_model) + print(f"Loaded model: {tts.vocoder_model}") do_gc() - return autoregressive_model_path + return vocoder_model def load_voicefixer(restart=False): global voicefixer diff --git a/src/webui.py b/src/webui.py index 9baf5f1..3090b5d 100755 --- a/src/webui.py +++ b/src/webui.py @@ -577,7 +577,6 @@ def setup_gradio(): gr.Checkbox(label="Force CPU for Conditioning Latents", value=args.force_cpu_for_conditioning_latents), gr.Checkbox(label="Do Not Load TTS On Startup", value=args.defer_tts_load), gr.Checkbox(label="Delete Non-Final Output", value=args.prune_nonfinal_outputs), - gr.Checkbox(label="Use BigVGAN Vocoder", value=args.use_bigvgan_vocoder), gr.Textbox(label="Device Override", value=args.device_override), ] with gr.Column(): @@ -590,10 +589,11 @@ def setup_gradio(): autoregressive_model_dropdown = gr.Dropdown(choices=autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else autoregressive_models[0]) + vocoder_models = gr.Dropdown(VOCODERS, label="Vocoder", value=args.vocoder_model if args.vocoder_model else VOCODERS[-1]) whisper_backend = gr.Dropdown(WHISPER_BACKENDS, label="Whisper Backends", value=args.whisper_backend) whisper_model_dropdown = gr.Dropdown(WHISPER_MODELS, label="Whisper Model", value=args.whisper_model) - exec_inputs = exec_inputs + [ autoregressive_model_dropdown, whisper_backend, whisper_model_dropdown, training_halfp, training_bnb ] + exec_inputs = exec_inputs + [ autoregressive_model_dropdown, vocoder_models, whisper_backend, whisper_model_dropdown, training_halfp, training_bnb ] with gr.Row(): autoregressive_models_update_button = gr.Button(value="Refresh Model List") @@ -626,6 +626,12 @@ def setup_gradio(): outputs=None ) + vocoder_models.change( + fn=update_vocoder_model, + inputs=vocoder_models, + outputs=None + ) + input_settings = [ text, delimiter, diff --git a/tortoise-tts b/tortoise-tts index 6fcd8c6..e2db36a 160000 --- a/tortoise-tts +++ b/tortoise-tts @@ -1 +1 @@ -Subproject commit 6fcd8c604f066e4e346da522bd14e6670395025f +Subproject commit e2db36af602297501132f7f68331755f5904825a