added VALL-E inference support (very rudimentary, gimped, but it will load a model trained on a config generated through the web UI)

config/ds_config.json

@@ -1,61 +0,0 @@
-{
-    "optimizer": {
-      "type": "AdamW",
-      "params": {
-        "lr": 2e-05,
-        "betas": [
-          0.9,
-          0.96
-        ],
-        "eps": 1e-07,
-        "weight_decay": 0.01
-      }
-    },
-    "scheduler":{
-        "type":"WarmupLR",
-        "params":{
-            "warmup_min_lr":0,
-            "warmup_max_lr":2e-5,
-            "warmup_num_steps":100,
-            "warmup_type":"linear"
-        }
-    },
-    "fp16":{
-        "enabled":true,
-        "loss_scale":0,
-        "loss_scale_window":1000,
-        "initial_scale_power":16,
-        "hysteresis":2,
-        "min_loss_scale":1
-    },
-    "autotuning":{
-        "enabled":false,
-        "results_dir":"./config/autotune/results",
-        "exps_dir":"./config/autotune/exps",
-        "overwrite":false,
-        "metric":"throughput",
-        "start_profile_step":10,
-        "end_profile_step":20,
-        "fast":false,
-        "max_train_batch_size":32,
-        "mp_size":1,
-        "num_tuning_micro_batch_sizes":3,
-        "tuner_type":"model_based",
-        "tuner_early_stopping":5,
-        "tuner_num_trials":50,
-        "arg_mappings":{
-            "train_micro_batch_size_per_gpu":"--per_device_train_batch_size",
-            "gradient_accumulation_steps ":"--gradient_accumulation_steps"
-        }
-    },
-    "zero_optimization":{
-        "stage":0,
-        "reduce_bucket_size":"auto",
-        "contiguous_gradients":true,
-        "sub_group_size":1e8,
-        "stage3_prefetch_bucket_size":"auto",
-        "stage3_param_persistence_threshold":"auto",
-        "stage3_max_live_parameters":"auto",
-        "stage3_max_reuse_distance":"auto"
-    }
-}

src/utils.py

@@ -22,6 +22,7 @@ import psutil
 import yaml
 import hashlib
 import string
+import random
 
 from tqdm import tqdm
 import torch
@@ -34,7 +35,7 @@ import pandas as pd
 from datetime import datetime
 from datetime import timedelta
 
-from tortoise.api import TextToSpeech, MODELS, get_model_path, pad_or_truncate
+from tortoise.api import TextToSpeech as TorToise_TTS, MODELS, get_model_path, pad_or_truncate
 from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir, get_voices
 from tortoise.utils.text import split_and_recombine_text
 from tortoise.utils.device import get_device_name, set_device_name, get_device_count, get_device_vram, get_device_batch_size, do_gc
@@ -68,6 +69,10 @@ try:
 	from vall_e.emb.qnt import encode as valle_quantize
 	from vall_e.emb.g2p import encode as valle_phonemize
 
+	from vall_e.inference import TTS as VALLE_TTS
+
+	import soundfile
+
 	VALLE_ENABLED = True
 except Exception as e:
 	pass
@@ -111,6 +116,12 @@ def resample( waveform, input_rate, output_rate=44100 ):
 	return RESAMPLERS[key]( waveform ), output_rate
 
 def generate(**kwargs):
+	if args.tts_backend == "tortoise":
+		return generate_tortoise(**kwargs)
+	if args.tts_backend == "vall-e":
+		return generate_valle(**kwargs)
+
+def generate_valle(**kwargs):
 	parameters = {}
 	parameters.update(kwargs)
 
@@ -140,7 +151,298 @@ def generate(**kwargs):
 	do_gc()
 
 	voice_samples = None
-	conditioning_latents =None
+	conditioning_latents = None
+	sample_voice = None
+
+	voice_cache = {}
+	def fetch_voice( voice ):
+		voice_dir = f'./voices/{voice}/'
+		files = [ f'{voice_dir}/{d}' for d in os.listdir(voice_dir) if d[-4:] == ".wav" ]
+		return files
+		# return random.choice(files)
+
+	def get_settings( override=None ):
+		settings = {
+			'ar_temp': float(parameters['temperature']),
+			'nar_temp': float(parameters['temperature']),
+			'max_ar_samples': parameters['num_autoregressive_samples'],
+		}
+
+		# could be better to just do a ternary on everything above, but i am not a professional
+		selected_voice = voice
+		if override is not None:
+			if 'voice' in override:
+				selected_voice = override['voice']
+
+			for k in override:
+				if k not in settings:
+					continue
+				settings[k] = override[k]
+
+		settings['reference'] = fetch_voice(voice=selected_voice)
+		return settings
+
+	if not parameters['delimiter']:
+		parameters['delimiter'] = "\n"
+	elif parameters['delimiter'] == "\\n":
+		parameters['delimiter'] = "\n"
+
+	if parameters['delimiter'] and parameters['delimiter'] != "" and parameters['delimiter'] in parameters['text']:
+		texts = parameters['text'].split(parameters['delimiter'])
+	else:
+		texts = split_and_recombine_text(parameters['text'])
+ 
+	full_start_time = time.time()
+ 
+	outdir = f"{args.results_folder}/{voice}/"
+	os.makedirs(outdir, exist_ok=True)
+
+	audio_cache = {}
+
+	volume_adjust = torchaudio.transforms.Vol(gain=args.output_volume, gain_type="amplitude") if args.output_volume != 1 else None
+
+	idx = 0
+	idx_cache = {}
+	for i, file in enumerate(os.listdir(outdir)):
+		filename = os.path.basename(file)
+		extension = os.path.splitext(filename)[1]
+		if extension != ".json" and extension != ".wav":
+			continue
+		match = re.findall(rf"^{voice}_(\d+)(?:.+?)?{extension}$", filename)
+		if match and len(match) > 0:
+			key = int(match[0])
+			idx_cache[key] = True
+
+	if len(idx_cache) > 0:
+		keys = sorted(list(idx_cache.keys()))
+		idx = keys[-1] + 1
+
+	idx = pad(idx, 4)
+
+	def get_name(line=0, candidate=0, combined=False):
+		name = f"{idx}"
+		if combined:
+			name = f"{name}_combined"
+		elif len(texts) > 1:
+			name = f"{name}_{line}"
+		if parameters['candidates'] > 1:
+			name = f"{name}_{candidate}"
+		return name
+
+	def get_info( voice, settings = None, latents = True ):
+		info = {}
+		info.update(parameters)
+
+		info['time'] = time.time()-full_start_time
+		info['datetime'] = datetime.now().isoformat()
+
+		info['progress'] = None
+		del info['progress']
+
+		if info['delimiter'] == "\n":
+			info['delimiter'] = "\\n"
+
+		if settings is not None:
+			for k in settings:
+				if k in info:
+					info[k] = settings[k]
+		return info
+
+	INFERENCING = True
+	for line, cut_text in enumerate(texts):	
+		progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]'
+		print(f"{progress.msg_prefix} Generating line: {cut_text}")
+		start_time = time.time()
+
+		# do setting editing
+		match = re.findall(r'^(\{.+\}) (.+?)$', cut_text) 
+		override = None
+		if match and len(match) > 0:
+			match = match[0]
+			try:
+				override = json.loads(match[0])
+				cut_text = match[1].strip()
+			except Exception as e:
+				raise Exception("Prompt settings editing requested, but received invalid JSON")
+
+		settings = get_settings( override=override )
+		reference = settings['reference']
+		settings.pop("reference")
+
+		gen = tts.inference(cut_text, reference, **settings )
+
+		run_time = time.time()-start_time
+		print(f"Generating line took {run_time} seconds")
+
+		if not isinstance(gen, list):
+			gen = [gen]
+
+		for j, g in enumerate(gen):
+			wav, sr = g
+			name = get_name(line=line, candidate=j)
+
+			settings['text'] = cut_text
+			settings['time'] = run_time
+			settings['datetime'] = datetime.now().isoformat()
+
+			# save here in case some error happens mid-batch
+			#torchaudio.save(f'{outdir}/{voice}_{name}.wav', wav.cpu(), sr)
+			soundfile.write(f'{outdir}/{voice}_{name}.wav', wav.cpu()[0,0], sr)
+			wav, sr = torchaudio.load(f'{outdir}/{voice}_{name}.wav')
+
+			audio_cache[name] = {
+				'audio': wav,
+				'settings': get_info(voice=override['voice'] if override and 'voice' in override else voice, settings=settings)
+			}
+
+	del gen
+	do_gc()
+	INFERENCING = False
+
+	for k in audio_cache:
+		audio = audio_cache[k]['audio']
+
+		audio, _ = resample(audio, tts.output_sample_rate, args.output_sample_rate)
+		if volume_adjust is not None:
+			audio = volume_adjust(audio)
+
+		audio_cache[k]['audio'] = audio
+		torchaudio.save(f'{outdir}/{voice}_{k}.wav', audio, args.output_sample_rate)
+
+	output_voices = []
+	for candidate in range(parameters['candidates']):
+		if len(texts) > 1:
+			audio_clips = []
+			for line in range(len(texts)):
+				name = get_name(line=line, candidate=candidate)
+				audio = audio_cache[name]['audio']
+				audio_clips.append(audio)
+			
+			name = get_name(candidate=candidate, combined=True)
+			audio = torch.cat(audio_clips, dim=-1)
+			torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, args.output_sample_rate)
+
+			audio = audio.squeeze(0).cpu()
+			audio_cache[name] = {
+				'audio': audio,
+				'settings': get_info(voice=voice),
+				'output': True
+			}
+		else:
+			name = get_name(candidate=candidate)
+			audio_cache[name]['output'] = True
+
+
+	if args.voice_fixer:
+		if not voicefixer:
+			progress(0, "Loading voicefix...")
+			load_voicefixer()
+
+		try:
+			fixed_cache = {}
+			for name in progress.tqdm(audio_cache, desc="Running voicefix..."):
+				del audio_cache[name]['audio']
+				if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
+					continue
+
+				path = f'{outdir}/{voice}_{name}.wav'
+				fixed = f'{outdir}/{voice}_{name}_fixed.wav'
+				voicefixer.restore(
+					input=path,
+					output=fixed,
+					cuda=get_device_name() == "cuda" and args.voice_fixer_use_cuda,
+					#mode=mode,
+				)
+				
+				fixed_cache[f'{name}_fixed'] = {
+					'settings': audio_cache[name]['settings'],
+					'output': True
+				}
+				audio_cache[name]['output'] = False
+			
+			for name in fixed_cache:
+				audio_cache[name] = fixed_cache[name]
+		except Exception as e:
+			print(e)
+			print("\nFailed to run Voicefixer")
+
+	for name in audio_cache:
+		if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
+			if args.prune_nonfinal_outputs:
+				audio_cache[name]['pruned'] = True
+				os.remove(f'{outdir}/{voice}_{name}.wav')
+			continue
+
+		output_voices.append(f'{outdir}/{voice}_{name}.wav')
+
+		if not args.embed_output_metadata:
+			with open(f'{outdir}/{voice}_{name}.json', 'w', encoding="utf-8") as f:
+				f.write(json.dumps(audio_cache[name]['settings'], indent='\t') )
+
+	if args.embed_output_metadata:
+		for name in progress.tqdm(audio_cache, desc="Embedding metadata..."):
+			if 'pruned' in audio_cache[name] and audio_cache[name]['pruned']:
+				continue
+
+			metadata = music_tag.load_file(f"{outdir}/{voice}_{name}.wav")
+			metadata['lyrics'] = json.dumps(audio_cache[name]['settings'])
+			metadata.save()
+ 
+	if sample_voice is not None:
+		sample_voice = (tts.input_sample_rate, sample_voice.numpy())
+
+	info = get_info(voice=voice, latents=False)
+	print(f"Generation took {info['time']} seconds, saved to '{output_voices[0]}'\n")
+
+	info['seed'] = usedSeed
+	if 'latents' in info:
+		del info['latents']
+
+	os.makedirs('./config/', exist_ok=True)
+	with open(f'./config/generate.json', 'w', encoding="utf-8") as f:
+		f.write(json.dumps(info, indent='\t') )
+
+	stats = [
+		[ parameters['seed'], "{:.3f}".format(info['time']) ]
+	]
+
+	return (
+		sample_voice,
+		output_voices,
+		stats,
+	)
+
+
+
+def generate_tortoise(**kwargs):
+	parameters = {}
+	parameters.update(kwargs)
+
+	voice = parameters['voice']
+	progress = parameters['progress'] if 'progress' in parameters else None
+	if parameters['seed'] == 0:
+		parameters['seed'] = None
+
+	usedSeed = parameters['seed']
+
+	global args
+	global tts
+
+	unload_whisper()
+	unload_voicefixer()
+
+	if not tts:
+		# should check if it's loading or unloaded, and load it if it's unloaded
+		if tts_loading:
+			raise Exception("TTS is still initializing...")
+		load_tts()
+	if hasattr(tts, "loading") and tts.loading:
+		raise Exception("TTS is still initializing...")
+
+	do_gc()
+
+	voice_samples = None
+	conditioning_latents = None
 	sample_voice = None
 
 	voice_cache = {}
@@ -295,11 +597,13 @@ def generate(**kwargs):
 	def get_info( voice, settings = None, latents = True ):
 		info = {}
 		info.update(parameters)
-		info['time'] = time.time()-full_start_time
 
+		info['time'] = time.time()-full_start_time
 		info['datetime'] = datetime.now().isoformat()
+
 		info['model'] = tts.autoregressive_model_path
 		info['model_hash'] = tts.autoregressive_model_hash 
+
 		info['progress'] = None
 		del info['progress']
 
@@ -381,9 +685,10 @@ def generate(**kwargs):
 
 			settings['text'] = cut_text
 			settings['time'] = run_time
-			settings['datetime'] = datetime.now().isoformat(),
-			settings['model'] = tts.autoregressive_model_path
-			settings['model_hash'] = tts.autoregressive_model_hash
+			settings['datetime'] = datetime.now().isoformat()
+			if args.tts_backend == "tortoise":
+				settings['model'] = tts.autoregressive_model_path
+				settings['model_hash'] = tts.autoregressive_model_hash
 
 			audio_cache[name] = {
 				'audio': audio,
@@ -745,8 +1050,8 @@ class TrainingState():
 			self.it_rate = f'{"{:.3f}".format(1/it_rate)}it/s' if 0 < it_rate and it_rate < 1 else f'{"{:.3f}".format(it_rate)}s/it'
 			self.it_rates += it_rate
 
-			epoch_rate = self.it_rates / self.it * self.steps
-			if epoch_rate > 0:
+			if self.it_rates > 0 and self.it * self.steps > 0:
+				epoch_rate = self.it_rates / self.it * self.steps
 				self.epoch_rate = f'{"{:.3f}".format(1/epoch_rate)}epoch/s' if 0 < epoch_rate and epoch_rate < 1 else f'{"{:.3f}".format(epoch_rate)}s/epoch'
 
 			try:
@@ -925,6 +1230,7 @@ class TrainingState():
 			self.it_rates = 0
 
 		unq = {}
+		averager = None
 
 		for log in logs:
 			with open(log, 'r', encoding="utf-8") as f:
@@ -941,16 +1247,18 @@ class TrainingState():
 				if line.find('Training Metrics:') >= 0:
 					split = line.split("Training Metrics:")[-1]
 					data = json.loads(split)
-					data['mode'] = "training"
+					
 					name = "train"
+					mode = "training"
 				elif line.find('Validation Metrics:') >= 0:
 					data = json.loads(line.split("Validation Metrics:")[-1])
-					data['mode'] = "validation"
 					if "it" not in data:
 						data['it'] = it
 					if "epoch" not in data:
 						data['epoch'] = epoch
+
 					name = data['name'] if 'name' in data else "val"
+					mode = "validation"
 				else:
 					continue
 
@@ -960,14 +1268,39 @@ class TrainingState():
 				it = data['it']
 				epoch = data['epoch']
 				
-				# this method should have it at least 
-				unq[f'{it}_{name}'] = data
+				if args.tts_backend == "vall-e":
+					if not averager or averager['key'] != f'{it}_{name}' or averager['mode'] != mode:
+						averager = {
+							'key': f'{it}_{name}',
+							'mode': mode,
+							"metrics": {}
+						}
+						for k in data:
+							if data[k] is None:
+								continue
+							averager['metrics'][k] = [ data[k] ]
+					else:
+						for k in data:
+							if data[k] is None:
+								continue
+							averager['metrics'][k].append( data[k] )
+
+					unq[f'{it}_{mode}_{name}'] = averager
+				else:
+					unq[f'{it}_{mode}_{name}'] = data
 
 				if update and it <= self.last_info_check_at:
 					continue
 		
 		for it in unq:
-			self.parse_metrics(unq[it])
+			if args.tts_backend == "vall-e":
+				stats = unq[it]
+				data = {k: sum(v) / len(v) for k, v in stats['metrics'].items()}
+				data['mode'] = stats
+				data['steps'] = len(stats['metrics']['it'])
+			else:
+				data = unq[it]
+			self.parse_metrics(data)
 
 		self.last_info_check_at = highest_step
 
@@ -1087,7 +1420,8 @@ def run_training(config_path, verbose=False, keep_x_past_checkpoints=0, progress
 
 
 	# ensure we have the dvae.pth
-	get_model_path('dvae.pth')
+	if args.tts_backend == "tortoise":
+		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()
@@ -2086,6 +2420,8 @@ def get_voice_list(dir=get_voice_dir(), append_defaults=False):
 		res = res + defaults
 	return res
 
+def get_valle_models(dir="./training/"):
+	return [ f'{dir}/{d}/config.yaml' for d in os.listdir(dir) if os.path.exists(f'{dir}/{d}/config.yaml') ]
 
 def get_autoregressive_models(dir="./models/finetunes/", prefixed=False, auto=False):
 	os.makedirs(dir, exist_ok=True)
@@ -2268,6 +2604,8 @@ def setup_args():
 		'tokenizer-json': None,
 
 		'phonemizer-backend': 'espeak',
+		
+		'valle-model': None,
 
 		'whisper-backend': 'openai/whisper',
 		'whisper-model': "base",
@@ -2319,6 +2657,8 @@ def setup_args():
 
 	parser.add_argument("--phonemizer-backend", default=default_arguments['phonemizer-backend'], help="Specifies which phonemizer backend to use.")
 	
+	parser.add_argument("--valle-model", default=default_arguments['valle-model'], help="Specifies which VALL-E model to use for sampling.")
+	
 	parser.add_argument("--whisper-backend", default=default_arguments['whisper-backend'], action='store_true', help="Picks which whisper backend to use (openai/whisper, lightmare/whispercpp)")
 	parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
 	parser.add_argument("--whisper-batchsize", type=int, default=default_arguments['whisper-batchsize'], help="Specifies batch size for WhisperX")
@@ -2389,6 +2729,8 @@ def get_default_settings( hypenated=True ):
 		'tokenizer-json': args.tokenizer_json,
 
 		'phonemizer-backend': args.phonemizer_backend,
+		
+		'valle-model': args.valle_model,
 
 		'whisper-backend': args.whisper_backend,
 		'whisper-model': args.whisper_model,
@@ -2439,6 +2781,8 @@ def update_args( **kwargs ):
 	args.tokenizer_json = settings['tokenizer_json']
 
 	args.phonemizer_backend = settings['phonemizer_backend']
+	
+	args.valle_model = settings['valle_model']
 
 	args.whisper_backend = settings['whisper_backend']
 	args.whisper_model = settings['whisper_model']
@@ -2553,49 +2897,60 @@ def version_check_tts( min_version ):
 		return True
 	return False
 
-def load_tts( restart=False, autoregressive_model=None, diffusion_model=None, vocoder_model=None, tokenizer_json=None ):
+def load_tts( restart=False, 
+	# TorToiSe configs
+	autoregressive_model=None, diffusion_model=None, vocoder_model=None, tokenizer_json=None,
+	# VALL-E configs
+	valle_model=None,
+):
 	global args
 	global tts
 
 	if restart:
 		unload_tts()
 
-	if autoregressive_model:
-		args.autoregressive_model = autoregressive_model
-	else:
-		autoregressive_model = args.autoregressive_model
+	tts_loading = True
+	if args.tts_backend == "tortoise":
+		if autoregressive_model:
+			args.autoregressive_model = autoregressive_model
+		else:
+			autoregressive_model = args.autoregressive_model
 
-	if autoregressive_model == "auto":
-		autoregressive_model = deduce_autoregressive_model()
+		if autoregressive_model == "auto":
+			autoregressive_model = deduce_autoregressive_model()
 
-	if diffusion_model:
-		args.diffusion_model = diffusion_model
-	else:
-		diffusion_model = args.diffusion_model
+		if diffusion_model:
+			args.diffusion_model = diffusion_model
+		else:
+			diffusion_model = args.diffusion_model
 
-	if vocoder_model:
-		args.vocoder_model = vocoder_model
-	else:
-		vocoder_model = args.vocoder_model
+		if vocoder_model:
+			args.vocoder_model = vocoder_model
+		else:
+			vocoder_model = args.vocoder_model
 
-	if tokenizer_json:
-		args.tokenizer_json = tokenizer_json
-	else:
-		tokenizer_json = args.tokenizer_json
+		if tokenizer_json:
+			args.tokenizer_json = tokenizer_json
+		else:
+			tokenizer_json = args.tokenizer_json
 
-	if get_device_name() == "cpu":
-		print("!!!! WARNING !!!! No GPU available in PyTorch. You may need to reinstall PyTorch.")
+		if get_device_name() == "cpu":
+			print("!!!! WARNING !!!! No GPU available in PyTorch. You may need to reinstall PyTorch.")
 
-	tts_loading = True
-	print(f"Loading TorToiSe... (AR: {autoregressive_model}, vocoder: {vocoder_model})")
-	tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=autoregressive_model, diffusion_model_path=diffusion_model, vocoder_model=vocoder_model, tokenizer_json=tokenizer_json, unsqueeze_sample_batches=args.unsqueeze_sample_batches)
-	tts_loading = False
+		print(f"Loading TorToiSe... (AR: {autoregressive_model}, diffusion: {diffusion_model}, vocoder: {vocoder_model})")
+		tts = TorToise_TTS(minor_optimizations=not args.low_vram, autoregressive_model_path=autoregressive_model, diffusion_model_path=diffusion_model, vocoder_model=vocoder_model, tokenizer_json=tokenizer_json, unsqueeze_sample_batches=args.unsqueeze_sample_batches)
+	elif args.tts_backend == "vall-e":
+		if valle_model:
+			args.valle_model = valle_model
+		else:
+			valle_model = args.valle_model
 
-	get_model_path('dvae.pth')
-	print("Loaded TorToiSe, ready for generation.")
-	return tts
+		print(f"Loading VALL-E... (Config: {valle_model})")
+		tts = VALLE_TTS(config=args.valle_model)
 
-setup_tortoise = load_tts
+	print("Loaded TTS, ready for generation.")
+	tts_loading = False
+	return tts
 
 def unload_tts():
 	global tts
@@ -2643,6 +2998,9 @@ def deduce_autoregressive_model(voice=None):
 	return get_model_path('autoregressive.pth')
 
 def update_autoregressive_model(autoregressive_model_path):
+	if args.tts_backend != "tortoise":
+		raise f"Unsupported backend: {args.tts_backend}"
+
 	match = re.findall(r'^\[[a-fA-F0-9]{8}\] (.+?)$', autoregressive_model_path)
 	if match:
 		autoregressive_model_path = match[0]
@@ -2677,6 +3035,9 @@ def update_autoregressive_model(autoregressive_model_path):
 	return autoregressive_model_path
 
 def update_diffusion_model(diffusion_model_path):
+	if args.tts_backend != "tortoise":
+		raise f"Unsupported backend: {args.tts_backend}"
+
 	match = re.findall(r'^\[[a-fA-F0-9]{8}\] (.+?)$', diffusion_model_path)
 	if match:
 		diffusion_model_path = match[0]
@@ -2711,6 +3072,9 @@ def update_diffusion_model(diffusion_model_path):
 	return diffusion_model_path
 
 def update_vocoder_model(vocoder_model):
+	if args.tts_backend != "tortoise":
+		raise f"Unsupported backend: {args.tts_backend}"
+
 	args.vocoder_model = vocoder_model
 	save_args_settings()
 	print(f'Stored vocoder model to settings: {vocoder_model}')
@@ -2733,6 +3097,9 @@ def update_vocoder_model(vocoder_model):
 	return vocoder_model
 
 def update_tokenizer(tokenizer_json):
+	if args.tts_backend != "tortoise":
+		raise f"Unsupported backend: {args.tts_backend}"
+
 	args.tokenizer_json = tokenizer_json
 	save_args_settings()
 	print(f'Stored tokenizer to settings: {tokenizer_json}')

src/webui.py

@@ -315,6 +315,8 @@ def setup_gradio():
 	voice_list = get_voice_list()
 	result_voices = get_voice_list(args.results_folder)
 	
+	valle_models = get_valle_models()
+
 	autoregressive_models = get_autoregressive_models()
 	diffusion_models = get_diffusion_models()
 	tokenizer_jsons = get_tokenizer_jsons()
@@ -337,11 +339,11 @@ def setup_gradio():
 				with gr.Column():
 					GENERATE_SETTINGS["delimiter"] = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
 
-					GENERATE_SETTINGS["emotion"] = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom", "None"], value="None", label="Emotion", type="value", interactive=True )
+					GENERATE_SETTINGS["emotion"] = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom", "None"], value="None", label="Emotion", type="value", interactive=True, visible=args.tts_backend=="tortoise" )
 					GENERATE_SETTINGS["prompt"] = gr.Textbox(lines=1, label="Custom Emotion", visible=False)
 					GENERATE_SETTINGS["voice"] = gr.Dropdown(choices=voice_list_with_defaults, label="Voice", type="value", value=voice_list_with_defaults[0]) # it'd be very cash money if gradio was able to default to the first value in the list without this shit
 					GENERATE_SETTINGS["mic_audio"] = gr.Audio( label="Microphone Source", source="microphone", type="filepath", visible=False )
-					GENERATE_SETTINGS["voice_latents_chunks"] = gr.Number(label="Voice Chunks", precision=0, value=0)
+					GENERATE_SETTINGS["voice_latents_chunks"] = gr.Number(label="Voice Chunks", precision=0, value=0, visible=args.tts_backend=="tortoise")
 					with gr.Row():
 						refresh_voices = gr.Button(value="Refresh Voice List")
 						recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents")
@@ -357,17 +359,17 @@ def setup_gradio():
 						outputs=GENERATE_SETTINGS["mic_audio"],
 					)
 				with gr.Column():
-					GENERATE_SETTINGS["candidates"] = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
+					GENERATE_SETTINGS["candidates"] = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates", visible=args.tts_backend=="tortoise")
 					GENERATE_SETTINGS["seed"] = gr.Number(value=0, precision=0, label="Seed")
 
 					preset = gr.Radio( ["Ultra Fast", "Fast", "Standard", "High Quality"], label="Preset", type="value", value="Ultra Fast" )
 
 					GENERATE_SETTINGS["num_autoregressive_samples"] = gr.Slider(value=16, minimum=2, maximum=512, step=1, label="Samples")
-					GENERATE_SETTINGS["diffusion_iterations"] = gr.Slider(value=30, minimum=0, maximum=512, step=1, label="Iterations")
+					GENERATE_SETTINGS["diffusion_iterations"] = gr.Slider(value=30, minimum=0, maximum=512, step=1, label="Iterations", visible=args.tts_backend=="tortoise")
 
 					GENERATE_SETTINGS["temperature"] = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
 					
-					show_experimental_settings = gr.Checkbox(label="Show Experimental Settings")
+					show_experimental_settings = gr.Checkbox(label="Show Experimental Settings", visible=args.tts_backend=="tortoise")
 					reset_generate_settings_button = gr.Button(value="Reset to Default")
 				with gr.Column(visible=False) as col:
 					experimental_column = col
@@ -606,10 +608,12 @@ def setup_gradio():
 					EXEC_SETTINGS['device_override'] = gr.Textbox(label="Device Override", value=args.device_override)
 
 					EXEC_SETTINGS['results_folder'] = gr.Textbox(label="Results Folder", value=args.results_folder)
-					
-				with gr.Column():
 					# EXEC_SETTINGS['tts_backend'] = gr.Dropdown(TTSES, label="TTS Backend", value=args.tts_backend if args.tts_backend else TTSES[0])
+					
+				with gr.Column(visible=args.tts_backend=="vall-e"):
+					EXEC_SETTINGS['valle_model'] = gr.Dropdown(choices=valle_models, label="VALL-E Model Config", value=args.valle_model if args.valle_model else valle_models[0])
 
+				with gr.Column(visible=args.tts_backend=="tortoise"):
 					EXEC_SETTINGS['autoregressive_model'] = gr.Dropdown(choices=["auto"] + autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else "auto")
 					EXEC_SETTINGS['diffusion_model'] = gr.Dropdown(choices=diffusion_models, label="Diffusion Model", value=args.diffusion_model if args.diffusion_model else diffusion_models[0])
 					EXEC_SETTINGS['vocoder_model'] = gr.Dropdown(VOCODERS, label="Vocoder", value=args.vocoder_model if args.vocoder_model else VOCODERS[-1])