forked from mrq/ai-voice-cloning
added dropdown to select autoregressive model for TTS, fixed a bug where the settings saveer constantly fires I hate gradio so much why are dropdown.change broken to contiuously fire and send an empty array
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models/finetunes/.gitkeep
Executable file
0
models/finetunes/.gitkeep
Executable file
57
src/utils.py
57
src/utils.py
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@ -62,6 +62,7 @@ def setup_args():
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'defer-tts-load': False,
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'device-override': None,
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'whisper-model': "base",
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'autoregressive-model': None,
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'concurrency-count': 2,
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'output-sample-rate': 44100,
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'output-volume': 1,
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@ -87,6 +88,7 @@ def setup_args():
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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("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch")
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parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
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parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.")
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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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parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)")
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@ -151,10 +153,8 @@ def generate(
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global args
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global tts
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try:
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tts
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except NameError:
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raise Exception("TTS is still initializing...")
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if not tts:
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raise Exception("TTS is uninitialized or still initializing...")
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if voice != "microphone":
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voices = [voice]
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@ -493,7 +493,7 @@ def setup_tortoise(restart=False):
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tts = None
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print("Initializating TorToiSe...")
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tts = TextToSpeech(minor_optimizations=not args.low_vram)
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tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model)
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get_model_path('dvae.pth')
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print("TorToiSe initialized, ready for generation.")
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return tts
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@ -720,7 +720,47 @@ def get_voice_list(dir=get_voice_dir()):
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os.makedirs(dir, exist_ok=True)
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return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ]) + ["microphone", "random"]
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def export_exec_settings( 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, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume, whisper_model ):
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def get_autoregressive_models(dir="./models/finetuned/"):
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os.makedirs(dir, exist_ok=True)
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return [get_model_path('autoregressive.pth')] + sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ])
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def update_autoregressive_model(path_name):
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global tts
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if not tts:
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raise Exception("TTS is uninitialized or still initializing...")
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print(f"Loading model: {path_name}")
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if hasattr(tts, 'load_autoregressive_model') and tts.load_autoregressive_model(path_name):
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args.autoregressive_model = path_name
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save_args_settings()
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# polyfill in case a user did NOT update the packages
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else:
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from tortoise.models.autoregressive import UnifiedVoice
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previous_path = tts.autoregressive_model_path
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tts.autoregressive_model_path = path_name if path_name and os.path.exists(path_name) else get_model_path('autoregressive.pth')
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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 previous_path != tts.autoregressive_model_path:
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args.autoregressive_model = path_name
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save_args_settings()
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print(f"Loaded model: {tts.autoregressive_model_path}")
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return path_name
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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, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume ):
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global args
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args.listen = listen
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@ -731,7 +771,6 @@ def export_exec_settings( listen, share, check_for_updates, models_from_local_on
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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.device_override = device_override
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args.whisper_model = whisper_model
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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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args.latents_lean_and_mean = latents_lean_and_mean
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@ -741,6 +780,9 @@ def export_exec_settings( listen, share, check_for_updates, models_from_local_on
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args.output_sample_rate = output_sample_rate
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args.output_volume = output_volume
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save_args_settings()
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def save_args_settings():
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settings = {
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'listen': None if args.listen else args.listen,
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'share': args.share,
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@ -751,6 +793,7 @@ def export_exec_settings( listen, share, check_for_updates, models_from_local_on
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'defer-tts-load': args.defer_tts_load,
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'device-override': args.device_override,
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'whisper-model': args.whisper_model,
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'autoregressive-model': args.autoregressive_model,
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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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'latents-lean-and-mean': args.latents_lean_and_mean,
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32
src/webui.py
32
src/webui.py
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@ -90,10 +90,8 @@ def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm
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global tts
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global args
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try:
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tts
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except NameError:
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raise gr.Error("TTS is still initializing...")
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if not tts:
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raise Exception("TTS is uninitialized or still initializing...")
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voice_samples, conditioning_latents = load_voice(voice, load_latents=False)
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@ -213,6 +211,14 @@ def update_voices():
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def history_copy_settings( voice, file ):
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return import_generate_settings( f"./results/{voice}/{file}" )
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def update_model_settings( autoregressive_model, whisper_model ):
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if args.autoregressive_model != autoregressive_model:
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update_autoregressive_model(autoregressive_model)
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args.whisper_model = whisper_model
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save_args_settings()
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def setup_gradio():
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global args
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global ui
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@ -370,13 +376,17 @@ def setup_gradio():
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gr.Number(label="Concurrency Count", precision=0, value=args.concurrency_count),
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gr.Number(label="Ouptut Sample Rate", precision=0, value=args.output_sample_rate),
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gr.Slider(label="Ouptut Volume", minimum=0, maximum=2, value=args.output_volume),
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gr.Dropdown(label="Whisper Model", value=args.whisper_model, choices=["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large"]),
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]
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autoregressive_model_dropdown = gr.Dropdown(get_autoregressive_models(), label="Autoregressive Model", value=args.autoregressive_model)
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whisper_model_dropdown = gr.Dropdown(["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large"], label="Whisper Model", value=args.whisper_model)
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save_settings_button = gr.Button(value="Save Settings")
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gr.Button(value="Check for Updates").click(check_for_updates)
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gr.Button(value="Reload TTS").click(reload_tts)
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gr.Button(value="(Re)Load TTS").click(reload_tts)
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for i in exec_inputs:
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i.change( fn=export_exec_settings, inputs=exec_inputs )
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i.change( fn=update_args, inputs=exec_inputs )
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# console_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
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@ -533,6 +543,14 @@ def setup_gradio():
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outputs=save_yaml_output #console_output
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)
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save_settings_button.click(update_model_settings,
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inputs=[
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autoregressive_model_dropdown,
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whisper_model_dropdown,
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],
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outputs=None
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)
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if os.path.isfile('./config/generate.json'):
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ui.load(import_generate_settings, inputs=None, outputs=input_settings)
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