Re-added missing joined variable

src/utils.py

@@ -42,8 +42,6 @@ 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
@@ -985,9 +983,6 @@ def run_training(config_path, verbose=False, gpus=1, keep_x_past_datasets=0, pro
 	global training_state
 	if training_state and training_state.process:
 		return "Training already in progress"
-
-	# ensure we have the dvae.pth
-	get_model_path('dvae.pth')
 	
 	# 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
 	torch.multiprocessing.freeze_support()
@@ -1198,6 +1193,10 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
 	with open(f'{outdir}/whisper.json', 'w', encoding="utf-8") as f:
 		f.write(json.dumps(results, indent='\t'))
 
+	joined = '\n'.join(transcription)
+	with open(f'{outdir}/train.txt', 'w', encoding="utf-8") as f:
+		f.write(joined)
+
 	unload_whisper()
 
 	return f"Processed dataset to: {outdir}\n{joined}"
@@ -1544,7 +1543,7 @@ def setup_args():
 		'defer-tts-load': False,
 		'device-override': None,
 		'prune-nonfinal-outputs': True,
-		'vocoder-model': VOCODERS[-1],
+		'use-bigvgan-vocoder': True,
 		'concurrency-count': 2,
 		'autocalculate-voice-chunk-duration-size': 0,
 		'output-sample-rate': 44100,
@@ -1581,7 +1580,7 @@ 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("--vocoder-model", default=default_arguments['vocoder-model'], action='store_true', help="Specifies with vocoder to use")
+	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("--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")
@@ -1625,7 +1624,7 @@ 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, 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 ):
+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 ):
 	global args
 
 	args.listen = listen
@@ -1636,6 +1635,7 @@ 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
@@ -1648,7 +1648,6 @@ 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
 
@@ -1668,6 +1667,7 @@ 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,
@@ -1680,7 +1680,6 @@ 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,
 
@@ -1796,11 +1795,11 @@ def load_tts( restart=False, model=None ):
 	if model:
 		args.autoregressive_model = model
 
-	print(f"Loading TorToiSe... (AR: {args.autoregressive_model}, vocoder: {args.vocoder_model})")
+	print(f"Loading TorToiSe... (using model: {args.autoregressive_model})")
 
 	tts_loading = True
 	try:
-		tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model, vocoder_model=args.vocoder_model)
+		tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model)
 	except Exception as e:
 		tts = TextToSpeech(minor_optimizations=not args.low_vram)
 		load_autoregressive_model(args.autoregressive_model)
@@ -1848,32 +1847,35 @@ def update_autoregressive_model(autoregressive_model_path):
 		return
 
 	print(f"Loading model: {autoregressive_model_path}")
-	tts.load_autoregressive_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