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

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)
@@ -1843,35 +1845,32 @@ def update_autoregressive_model(autoregressive_model_path):
 		return
 
 	print(f"Loading model: {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)
-
-		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)
-
-	if not hasattr(tts, 'autoregressive_model_hash'):
-		tts.autoregressive_model_hash = hash_file(autoregressive_model_path)
-
 	print(f"Loaded model: {tts.autoregressive_model_path}")
 
 	do_gc()
 	
 	return autoregressive_model_path
 
+def update_vocoder_model(vocoder_model):
+	args.vocoder_model = vocoder_model
+	save_args_settings()
+	print(f'Stored vocoder model to settings: {vocoder_model}')
+
+	global tts
+	if not tts:
+		if tts_loading:
+			raise Exception("TTS is still initializing...")
+		return
+
+	print(f"Loading model: {vocoder_model}")
+	tts.load_vocoder_model(vocoder_model)
+	print(f"Loaded model: {tts.vocoder_model}")
+
+	do_gc()
+	
+	return vocoder_model
+
 def load_voicefixer(restart=False):
 	global voicefixer
 

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,

tortoise-tts

@@ -1 +1 @@
-Subproject commit 6fcd8c604f066e4e346da522bd14e6670395025f
+Subproject commit e2db36af602297501132f7f68331755f5904825a