Added optional whispercpp update functionality

.gitignore

@@ -1,7 +1,8 @@
 # ignores user files
-/tortoise-venv/
-/tortoise/voices/
-/models/
+/venv/
+/voices/*
+/models/*
+/training/*
 /config/*
 
 # Byte-compiled / optimized / DLL files

dlas

@@ -1 +1 @@
-Subproject commit 0f04206aa20b1ab632c0cbf7bb6a43d5c1fd9eb0
+Subproject commit 71cc43e65cd47c6704d20c99006a3e78feb2400d

setup-cuda.bat

@@ -7,12 +7,12 @@ python -m pip install --upgrade pip
 python -m pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117
 python -m pip install -r .\dlas\requirements.txt
 python -m pip install -r .\tortoise-tts\requirements.txt
-python -m pip install -r .\requirements.txt
 python -m pip install -e .\tortoise-tts\
+python -m pip install -r .\requirements.txt
 
 xcopy .\dlas\bitsandbytes_windows\* .\venv\Lib\site-packages\bitsandbytes\. /Y
 xcopy .\dlas\bitsandbytes_windows\cuda_setup\* .\venv\Lib\site-packages\bitsandbytes\cuda_setup\. /Y
 xcopy .\dlas\bitsandbytes_windows\nn\* .\venv\Lib\site-packages\bitsandbytes\nn\. /Y
 
-deactivate
 pause
+deactivate

setup-cuda.sh

@@ -1,14 +1,17 @@
 #!/bin/bash
+# get local dependencies
 git submodule init
 git submodule update --remote
-
+# setup venv
 python3 -m venv venv
 source ./venv/bin/activate
-python3 -m pip install --upgrade pip
+python3 -m pip install --upgrade pip # just to be safe
 # CUDA
-pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116
-python3 -m pip install -r ./dlas/requirements.txt
-python3 -m pip install -r ./tortoise-tts/requirements.txt
-python3 -m pip install -r ./requirements.txt
-python3 -m pip install -e ./tortoise-tts/
+pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117
+# install requirements
+python3 -m pip install -r ./dlas/requirements.txt # instal DLAS requirements
+python3 -m pip install -r ./tortoise-tts/requirements.txt # install TorToiSe requirements
+python3 -m pip install -e ./tortoise-tts/ # install TorToiSe
+python3 -m pip install -r ./requirements.txt # install local requirements
+
 deactivate

setup-directml.bat

@@ -4,10 +4,11 @@ git submodule update --remote
 python -m venv venv
 call .\venv\Scripts\activate.bat
 python -m pip install --upgrade pip
-python -m pip install torch torchvision torchaudio torch-directml==0.1.13.1.dev230119
+python -m pip install torch torchvision torchaudio torch-directml
 python -m pip install -r .\dlas\requirements.txt
 python -m pip install -r .\tortoise-tts\requirements.txt
-python -m pip install -r .\requirements.txt
 python -m pip install -e .\tortoise-tts\
-deactivate
+python -m pip install -r .\requirements.txt
+
 pause
+deactivate

setup-rocm-bnb.sh

@@ -0,0 +1,8 @@
+#!/bin/bash
+source ./venv/bin/activate
+git clone https://git.ecker.tech/mrq/bitsandbytes-rocm
+cd bitsandbytes-rocm
+make hip
+CUDA_VERSION=gfx1030 python setup.py install # assumes you're using a 6XXX series card
+python3 -m bitsandbytes # to validate it works
+cd ..

setup-rocm.sh

@@ -1,14 +1,19 @@
 #!/bin/bash
+# get local dependencies
 git submodule init
 git submodule update --remote
-
+# setup venv
 python3 -m venv venv
 source ./venv/bin/activate
-python3 -m pip install --upgrade pip
+python3 -m pip install --upgrade pip # just to be safe
 # ROCM
-pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.1.1 # 5.2 does not work for me desu
-python3 -m pip install -r ./dlas/requirements.txt
-python3 -m pip install -r ./tortoise-tts/requirements.txt
-python3 -m pip install -r ./requirements.txt
-python3 -m pip install -e ./tortoise-tts/
+pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.1.1 # 5.2 does not work for me desu
+# install requirements
+python3 -m pip install -r ./dlas/requirements.txt # instal DLAS requirements
+python3 -m pip install -r ./tortoise-tts/requirements.txt # install TorToiSe requirements
+python3 -m pip install -e ./tortoise-tts/ # install TorToiSe
+python3 -m pip install -r ./requirements.txt # install local requirements
+# swap to ROCm version of BitsAndBytes
+pip3 uninstall -y bitsandbytes
+./setup-rocm-bnb.sh
 deactivate

src/train.py

@@ -18,9 +18,12 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vit_latent.yml', nargs='+') # ugh
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
+    parser.add_argument('--local_rank', type=int, help='Rank Number') 
     args = parser.parse_args()
     args.opt = " ".join(args.opt) # absolutely disgusting
 
+    os.environ['LOCAL_RANK'] = str(args.local_rank)
+
     with open(args.opt, 'r') as file:
         opt_config = yaml.safe_load(file)
 
@@ -71,7 +74,7 @@ def train(yaml, launcher='none'):
         print('Disabled distributed training.')
     else:
         opt['dist'] = True
-        init_dist('nccl')
+        tr.init_dist('nccl')
         trainer.world_size = torch.distributed.get_world_size()
         trainer.rank = torch.distributed.get_rank()
         torch.cuda.set_device(torch.distributed.get_rank())

src/utils.py

@@ -97,7 +97,11 @@ def generate(
 		voice_samples, conditioning_latents = None, tts.get_random_conditioning_latents()
 	else:
 		progress(0, desc="Loading voice...")
-		voice_samples, conditioning_latents = load_voice(voice)
+		# nasty check for users that, for whatever reason, updated the web UI but not mrq/tortoise-tts
+		if hasattr(tts, 'autoregressive_model_hash'):
+			voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
+		else:
+			voice_samples, conditioning_latents = load_voice(voice)
 
 	if voice_samples and len(voice_samples) > 0:
 		sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu()
@@ -107,7 +111,10 @@ def generate(
 			conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
 			
 		if voice != "microphone":
-			torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
+			if hasattr(tts, 'autoregressive_model_hash'):
+				torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
+			else:
+				torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
 		voice_samples = None
 	else:
 		if conditioning_latents is not None:
@@ -371,8 +378,16 @@ def generate(
 
 
 	if voice and voice != "random" and conditioning_latents is not None:
-		with open(f'{get_voice_dir()}/{voice}/cond_latents.pth', 'rb') as f:
-			info['latents'] = base64.b64encode(f.read()).decode("ascii")
+		latents_path = f'{get_voice_dir()}/{voice}/cond_latents.pth'
+
+		if hasattr(tts, 'autoregressive_model_hash'):
+			latents_path = f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth'
+
+		try:
+			with open(latents_path, 'rb') as f:
+				info['latents'] = base64.b64encode(f.read()).decode("ascii")
+		except Exception as e:
+			pass
 
 	if args.embed_output_metadata:
 		for name in progress.tqdm(audio_cache, desc="Embedding metadata..."):
@@ -413,6 +428,32 @@ def cancel_generate():
 	import tortoise.api
 	tortoise.api.STOP_SIGNAL = True
 
+def hash_file(path, algo="md5", buffer_size=0):
+	import hashlib
+
+	hash = None
+	if algo == "md5":
+		hash = hashlib.md5()
+	elif algo == "sha1":
+		hash = hashlib.sha1()
+	else:
+		raise Exception(f'Unknown hash algorithm specified: {algo}')
+
+	if not os.path.exists(path):
+		raise Exception(f'Path not found: {path}')
+
+	with open(path, 'rb') as f:
+		if buffer_size > 0:
+			while True:
+				data = f.read(buffer_size)
+				if not data:
+					break
+				hash.update(data)
+		else:
+			hash.update(f.read())
+
+	return "{0}".format(hash.hexdigest())
+
 def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
 	global tts
 	global args
@@ -435,15 +476,16 @@ def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm
 	if len(conditioning_latents) == 4:
 		conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
 			
-	torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
+	if hasattr(tts, 'autoregressive_model_hash'):
+		torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
+	else:
+		torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
 
 	return voice
 
 # superfluous, but it cleans up some things
 class TrainingState():
-	def __init__(self, config_path, keep_x_past_datasets=0):
-		self.cmd = ['train.bat', config_path] if os.name == "nt" else ['bash', './train.sh', config_path]
-
+	def __init__(self, config_path, keep_x_past_datasets=0, start=True, gpus=1):
 		# parse config to get its iteration
 		with open(config_path, 'r') as file:
 			self.config = yaml.safe_load(file)
@@ -487,17 +529,22 @@ class TrainingState():
 		self.eta = "?"
 		self.eta_hhmmss = "?"
 
+		self.last_info_check_at = 0
 		self.losses = []
 
 		self.load_losses()
-		self.cleanup_old(keep=keep_x_past_datasets)
-		self.spawn_process()
+		if keep_x_past_datasets > 0:
+			self.cleanup_old(keep=keep_x_past_datasets)
+		if start:
+			self.spawn_process(config_path=config_path, gpus=gpus)
+
+	def spawn_process(self, config_path, gpus=1):
+		self.cmd = ['train.bat', config_path] if os.name == "nt" else ['bash', './train.sh', str(int(gpus)), config_path]
 
-	def spawn_process(self):
 		print("Spawning process: ", " ".join(self.cmd))
 		self.process = subprocess.Popen(self.cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
 
-	def load_losses(self):
+	def load_losses(self, update=False):
 		if not os.path.isdir(f'{self.dataset_dir}/tb_logger/'):
 			return
 		try:
@@ -506,18 +553,26 @@ class TrainingState():
 		except Exception as e:
 			use_tensorboard = False
 
+		keys = ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total']
+		infos = {}
+		highest_step = self.last_info_check_at
+
 		if use_tensorboard:
 			logs = sorted([f'{self.dataset_dir}/tb_logger/{d}' for d in os.listdir(f'{self.dataset_dir}/tb_logger/') if d[:6] == "events" ])
-			infos = {}
+			if update:
+				logs = [logs[-1]]
+
 			for log in logs:
 				try:
 					ea = event_accumulator.EventAccumulator(log, size_guidance={event_accumulator.SCALARS: 0})
 					ea.Reload()
 
-					keys = ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total']
 					for key in keys:
 						scalar = ea.Scalars(key)
 						for s in scalar:
+							if update and s.step <= self.last_info_check_at:
+								continue
+							highest_step = max( highest_step, s.step )
 							self.losses.append( { "step": s.step, "value": s.value, "type": key } )
 				except Exception as e:
 					print("Failed to parse event log:", log)
@@ -525,7 +580,9 @@ class TrainingState():
 
 		else:
 			logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])
-			infos = {}
+			if update:
+				logs = [logs[-1]]
+
 			for log in logs:
 				with open(log, 'r', encoding="utf-8") as f:
 					lines = f.readlines()
@@ -546,9 +603,13 @@ class TrainingState():
 
 			for k in infos:
 				if 'loss_gpt_total' in infos[k]:
-					self.losses.append({ "step": int(k), "value": infos[k]['loss_text_ce'], "type": "text_ce" })
-					self.losses.append({ "step": int(k), "value": infos[k]['loss_mel_ce'], "type": "mel_ce" })
-					self.losses.append({ "step": int(k), "value": infos[k]['loss_gpt_total'], "type": "gpt_total" })
+					for key in keys:
+						if update and int(k) <= self.last_info_check_at:
+							continue
+						highest_step = max( highest_step, s.step )
+						self.losses.append({ "step": int(k), "value": infos[k][key], "type": key })
+
+		self.last_info_check_at = highest_step
 
 	def cleanup_old(self, keep=2):
 		if keep <= 0:
@@ -581,6 +642,7 @@ class TrainingState():
 			if line.find('Start training from epoch') >= 0:
 				self.epoch_time_start = time.time()
 				self.training_started = True # could just leverage the above variable, but this is python, and there's no point in these aggressive microoptimizations
+				should_return = True
 
 				match = re.findall(r'epoch: ([\d,]+)', line)
 				if match and len(match) > 0:
@@ -662,12 +724,15 @@ class TrainingState():
 							
 				if 'loss_gpt_total' in self.info:
 					self.status = f"Total loss at epoch {self.epoch}: {self.info['loss_gpt_total']}"
-
-					self.losses.append({ "step": self.it, "value": self.info['loss_text_ce'], "type": "text_ce" })
-					self.losses.append({ "step": self.it, "value": self.info['loss_mel_ce'], "type": "mel_ce" })
-					self.losses.append({ "step": self.it, "value": self.info['loss_gpt_total'], "type": "gpt_total" })
-
+					"""
+					self.losses.append({ "step": self.it, "value": self.info['loss_text_ce'], "type": "loss_text_ce" })
+					self.losses.append({ "step": self.it, "value": self.info['loss_mel_ce'], "type": "loss_mel_ce" })
+					self.losses.append({ "step": self.it, "value": self.info['loss_gpt_total'], "type": "loss_gpt_total" })
+					"""
 					should_return = True
+
+				self.load_losses(update=True)
+
 			elif line.find('Saving models and training states') >= 0:
 				self.checkpoint = self.checkpoint + 1
 
@@ -688,7 +753,7 @@ class TrainingState():
 		if should_return:
 			return "".join(self.buffer)
 
-def run_training(config_path, verbose=False, buffer_size=8, keep_x_past_datasets=0, progress=gr.Progress(track_tqdm=True)):
+def run_training(config_path, verbose=False, gpus=1, buffer_size=8, keep_x_past_datasets=0, progress=gr.Progress(track_tqdm=True)):
 	global training_state
 	if training_state and training_state.process:
 		return "Training already in progress"
@@ -700,7 +765,7 @@ def run_training(config_path, verbose=False, buffer_size=8, keep_x_past_datasets
 	unload_whisper()
 	unload_voicefixer()
 
-	training_state = TrainingState(config_path=config_path, keep_x_past_datasets=keep_x_past_datasets)
+	training_state = TrainingState(config_path=config_path, keep_x_past_datasets=keep_x_past_datasets, gpus=gpus)
 
 	for line in iter(training_state.process.stdout.readline, ""):
 		
@@ -723,11 +788,21 @@ def get_training_losses():
 		return
 	return pd.DataFrame(training_state.losses)
 
-def update_training_dataplot():
+def update_training_dataplot(config_path=None):
 	global training_state
-	if not training_state or not training_state.losses:
-		return
-	return gr.LinePlot.update(value=pd.DataFrame(training_state.losses))
+	update = None
+
+	if not training_state:
+		if config_path:
+			training_state = TrainingState(config_path=config_path, start=False)
+			if training_state.losses:
+				update = gr.LinePlot.update(value=pd.DataFrame(training_state.losses))
+			del training_state
+			training_state = None
+	elif training_state.losses:
+		update = gr.LinePlot.update(value=pd.DataFrame(training_state.losses))
+
+	return update
 
 def reconnect_training(verbose=False, buffer_size=8, progress=gr.Progress(track_tqdm=True)):
 	global training_state
@@ -823,8 +898,11 @@ def prepare_dataset( files, outdir, language=None, progress=None ):
 
 			torchaudio.save(f"{outdir}/{sliced_name}", sliced_waveform, sampling_rate)
 
-			transcription.append(f"{sliced_name}|{segment['text'].strip()}")
 			idx = idx + 1
+			line = f"{sliced_name}|{segment['text'].strip()}"
+			transcription.append(line)
+			with open(f'{outdir}/train.txt', 'a', encoding="utf-8") as f:
+				f.write(f'{line}\n')
 	
 	with open(f'{outdir}/whisper.json', 'w', encoding="utf-8") as f:
 		f.write(json.dumps(results, indent='\t'))
@@ -1035,32 +1113,6 @@ def get_voice_list(dir=get_voice_dir(), append_defaults=False):
 		res = res + ["random", "microphone"]
 	return res
 
-def hash_file(path, algo="md5", buffer_size=0):
-	import hashlib
-
-	hash = None
-	if algo == "md5":
-		hash = hashlib.md5()
-	elif algo == "sha1":
-		hash = hashlib.sha1()
-	else:
-		raise Exception(f'Unknown hash algorithm specified: {algo}')
-
-	if not os.path.exists(path):
-		raise Exception(f'Path not found: {path}')
-
-	with open(path, 'rb') as f:
-		if buffer_size > 0:
-			while True:
-				data = f.read(buffer_size)
-				if not data:
-					break
-				hash.update(data)
-		else:
-			hash.update(f.read())
-
-	return "{0}".format(hash.hexdigest())
-
 def get_autoregressive_models(dir="./models/finetunes/", prefixed=False):
 	os.makedirs(dir, exist_ok=True)
 	base = [get_model_path('autoregressive.pth')]
@@ -1190,6 +1242,7 @@ def setup_args():
 		'defer-tts-load': False,
 		'device-override': None,
 		'prune-nonfinal-outputs': True,
+		'use-bigvgan-vocoder': True,
 		'concurrency-count': 2,
 		'output-sample-rate': 44100,
 		'output-volume': 1,
@@ -1225,6 +1278,7 @@ 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("--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")
@@ -1246,12 +1300,13 @@ def setup_args():
 	if not args.device_override:
 		set_device_name(args.device_override)
 
+
 	args.listen_host = None
 	args.listen_port = None
 	args.listen_path = None
 	if args.listen:
 		try:
-			match = re.findall(r"^(?:(.+?):(\d+))?(\/.+?)?$", args.listen)[0]
+			match = re.findall(r"^(?:(.+?):(\d+))?(\/.*?)?$", args.listen)[0]
 
 			args.listen_host = match[0] if match[0] != "" else "127.0.0.1"
 			args.listen_port = match[1] if match[1] != "" else None
@@ -1264,7 +1319,7 @@ 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, output_sample_rate, output_volume, autoregressive_model, whisper_model, whisper_cpp, 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, output_sample_rate, output_volume, autoregressive_model, whisper_model, whisper_cpp, training_default_halfp, training_default_bnb ):
 	global args
 
 	args.listen = listen
@@ -1275,6 +1330,7 @@ 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
@@ -1297,7 +1353,7 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v
 def save_args_settings():
 	global args
 	settings = {
-		'listen': None if args.listen else args.listen,
+		'listen': None if not args.listen else args.listen,
 		'share': args.share,
 		'low-vram':args.low_vram,
 		'check-for-updates':args.check_for_updates,
@@ -1305,6 +1361,7 @@ 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,
@@ -1419,7 +1476,7 @@ def load_tts( restart=False, model=None ):
 
 	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, use_bigvgan=args.use_bigvgan_vocoder)
 	except Exception as e:
 		tts = TextToSpeech(minor_optimizations=not args.low_vram)
 		load_autoregressive_model(args.autoregressive_model)
@@ -1533,7 +1590,7 @@ def load_whisper_model(name=None, progress=None, language=b'en'):
 	notify_progress(f"Loading Whisper model: {args.whisper_model}", progress)
 	if args.whisper_cpp:
 		from whispercpp import Whisper
-		whisper_model = Whisper(name, models_dir='./models/', language=language)
+		whisper_model = Whisper(name, models_dir='./models/', language=language.encode('ascii'))
 	else:
 		import whisper
 		whisper_model = whisper.load_model(args.whisper_model)

src/webui.py

@@ -527,16 +527,8 @@ def setup_gradio():
 				with gr.Row():
 					with gr.Column():
 						training_configs = gr.Dropdown(label="Training Configuration", choices=get_training_list())
-						refresh_configs = gr.Button(value="Refresh Configurations")
 						with gr.Row():
-							start_training_button = gr.Button(value="Train")
-							stop_training_button = gr.Button(value="Stop")
-							reconnect_training_button = gr.Button(value="Reconnect")
-					with gr.Column():
-						training_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
-						verbose_training = gr.Checkbox(label="Verbose Console Output", value=True)
-						training_buffer_size = gr.Slider(label="Console Buffer Size", minimum=4, maximum=32, value=8)
-						training_keep_x_past_datasets = gr.Slider(label="Keep X Previous States", minimum=0, maximum=8, value=0, step=1)
+							refresh_configs = gr.Button(value="Refresh Configurations")
 						
 						training_loss_graph = gr.LinePlot(label="Training Metrics",
 							x="step",
@@ -545,8 +537,20 @@ def setup_gradio():
 							color="type",
 							tooltip=['step', 'value', 'type'],
 							width=600,
-							height=350
+							height=350,
 						)
+						view_losses = gr.Button(value="View Losses")
+
+					with gr.Column():
+						training_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
+						verbose_training = gr.Checkbox(label="Verbose Console Output", value=True)
+						training_buffer_size = gr.Slider(label="Console Buffer Size", minimum=4, maximum=32, value=8)
+						training_keep_x_past_datasets = gr.Slider(label="Keep X Previous States", minimum=0, maximum=8, value=0, step=1)
+						training_gpu_count = gr.Number(label="GPUs", value=1)
+						with gr.Row():
+							start_training_button = gr.Button(value="Train")
+							stop_training_button = gr.Button(value="Stop")
+							reconnect_training_button = gr.Button(value="Reconnect")
 		with gr.Tab("Settings"):
 			with gr.Row():
 				exec_inputs = []
@@ -564,6 +568,7 @@ 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():
@@ -748,6 +753,7 @@ def setup_gradio():
 			inputs=[
 				training_configs,
 				verbose_training,
+				training_gpu_count,
 				training_buffer_size,
 				training_keep_x_past_datasets,
 			],
@@ -763,6 +769,17 @@ def setup_gradio():
 			],
 			show_progress=False,
 		)
+
+		view_losses.click(
+			fn=update_training_dataplot,
+			inputs=[
+				training_configs
+			],
+			outputs=[
+				training_loss_graph,
+			],
+		)
+
 		stop_training_button.click(stop_training,
 			inputs=None,
 			outputs=training_output #console_output

start.bat

@@ -1,5 +1,4 @@
 call .\venv\Scripts\activate.bat
 set PATH=.\bin\;%PATH%
 python .\src\main.py %*
-deactivate
 pause

tortoise-tts

@@ -1 +1 @@
-Subproject commit 7cc0250a1a559da90965812fdefcba0d54a59c41
+Subproject commit aca32a71f798ebd8487c113d41d1b4e9ee15c315

train.bat

@@ -1,4 +1,4 @@
 call .\venv\Scripts\activate.bat
 python ./src/train.py -opt "%1"
-deactivate
 pause
+deactivate

train.sh

@@ -1,4 +1,13 @@
 #!/bin/bash
 source ./venv/bin/activate
-python3 ./src/train.py -opt "$1"
+
+GPUS=$1
+CONFIG=$2
+PORT=1234
+
+if (( $GPUS > 1 )); then
+	python3 -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT ./src/train.py -opt "$CONFIG" --launcher=pytorch
+else
+	python3 ./src/train.py -opt "$CONFIG"
+fi
 deactivate

update-force.bat

@@ -1,3 +1,15 @@
 git fetch --all
 git reset --hard origin/master
 call .\update.bat
+
+python -m venv venv
+call .\venv\Scripts\activate.bat
+
+python -m pip install --upgrade pip
+python -m pip install -U -r .\dlas\requirements.txt
+python -m pip install -U -r .\tortoise-tts\requirements.txt
+python -m pip install -U -e .\tortoise-tts 
+python -m pip install -U -r .\requirements.txt
+
+pause
+deactivate

update-force.sh

@@ -1,4 +1,17 @@
 #!/bin/bash
 git fetch --all
 git reset --hard origin/master
+
 ./update.sh
+
+# force install requirements
+python3 -m venv venv
+source ./venv/bin/activate
+
+python3 -m pip install --upgrade pip
+python3 -m pip install -r ./dlas/requirements.txt
+python3 -m pip install -r ./tortoise-tts/requirements.txt
+python3 -m pip install -e ./tortoise-tts 
+python3 -m pip install -r ./requirements.txt
+
+deactivate

update.bat

@@ -1,14 +1,2 @@
 git pull
 git submodule update --remote
-
-python -m venv venv
-call .\venv\Scripts\activate.bat
-
-python -m pip install --upgrade pip
-python -m pip install -r .\dlas\requirements.txt
-python -m pip install -r .\tortoise-tts\requirements.txt
-python -m pip install -e .\tortoise-tts 
-python -m pip install -r .\requirements.txt
-
-deactivate
-pause

update.sh

@@ -2,14 +2,6 @@
 git pull
 git submodule update --remote
 
-python3 -m venv venv
-source ./venv/bin/activate
-
-python3 -m pip install --upgrade pip
-python3 -m pip install -r ./dlas/requirements.txt
-python3 -m pip install -r ./tortoise-tts/requirements.txt
-python3 -m pip install -e ./tortoise-tts 
-python3 -m pip install -r ./requirements.txt
-
+if python -m pip show whispercpp &>/dev/null; then python -m pip install -U git+https://git.ecker.tech/lightmare/whispercpp.py; fi
 
 deactivate