I don't remember.

src/utils.py

@@ -34,8 +34,6 @@ from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_
 from tortoise.utils.text import split_and_recombine_text
 from tortoise.utils.device import get_device_name, set_device_name
 
-import whisper
-
 MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
 
 args = None
@@ -46,7 +44,6 @@ voicefixer = None
 whisper_model = None
 training_state = None
 
-
 def generate(
 	text,
 	delimiter,
@@ -501,9 +498,12 @@ class TrainingState():
 				match = re.findall(r'iter: ([\d,]+)', line)
 				if match and len(match) > 0:
 					self.it = int(match[0].replace(",", ""))
+
+				self.checkpoints = int((self.its - self.it) / self.config['logger']['save_checkpoint_freq'])
 		else:
 			lapsed = False
 
+			message = None
 			if line.find('%|') > 0:
 				match = re.findall(r'(\d+)%\|(.+?)\| (\d+|\?)\/(\d+|\?) \[(.+?)<(.+?), +(.+?)\]', line)
 				if match and len(match) > 0:
@@ -516,8 +516,6 @@ class TrainingState():
 					until = match[5]
 					rate = match[6]
 
-					epoch_percent = self.it / float(self.its) # self.epoch / float(self.epochs)
-
 					last_step = self.last_step
 					self.last_step = step
 					if last_step < step:
@@ -530,11 +528,13 @@ class TrainingState():
 					self.it_time_delta = self.it_time_end-self.it_time_start
 					self.it_time_start = time.time()
 					try:
-						rate = f'[{"{:.3f}".format(self.it_time_delta)}s/it]' if self.it_time_delta >= 1 else f'[{"{:.3f}".format(1/self.it_time_delta)}it/s]'
+						rate = f'{"{:.3f}".format(self.it_time_delta)}s/it' if self.it_time_delta >= 1 else f'{"{:.3f}".format(1/self.it_time_delta)}it/s'
 						self.it_rate = rate
 					except Exception as e:
 						pass
 
+					message = f'[{self.epoch}/{self.epochs}, {self.it}/{self.its}, {step}/{steps}] [ETA: {self.eta_hhmmss}] [{self.epoch_rate}, {self.it_rate}] {self.status}'
+					
 					"""
 					# I wanted frequently updated ETA, but I can't wrap my noggin around getting it to work on an empty belly
 					# will fix later
@@ -550,13 +550,6 @@ class TrainingState():
 						pass
 					"""
 
-					message = f'[{self.epoch}/{self.epochs}] [{self.it}/{self.its}] [ETA: {self.eta_hhmmss}] {self.epoch_rate} / {self.it_rate} {self.status}'
-					if progress is not None:
-						progress(epoch_percent, message)
-
-					# print(f'{"{:.3f}".format(percent*100)}% {message}')
-					self.buffer.append(f'[{"{:.3f}".format(epoch_percent*100)}% / {"{:.3f}".format(percent*100)}%] {message}')
-
 			if lapsed:
 				self.epoch = self.epoch + 1
 				self.it = int(self.epoch * (self.dataset_size / self.batch_size))
@@ -564,7 +557,7 @@ class TrainingState():
 				self.epoch_time_end = time.time()
 				self.epoch_time_delta = self.epoch_time_end-self.epoch_time_start
 				self.epoch_time_start = time.time()
-				self.epoch_rate = f'[{"{:.3f}".format(self.epoch_time_delta)}s/epoch]' if self.epoch_time_delta >= 1 else f'[{"{:.3f}".format(1/self.epoch_time_delta)}epoch/s]' # I doubt anyone will have it/s rates, but its here
+				self.epoch_rate = f'{"{:.3f}".format(self.epoch_time_delta)}s/epoch' if self.epoch_time_delta >= 1 else f'{"{:.3f}".format(1/self.epoch_time_delta)}epoch/s' # I doubt anyone will have it/s rates, but its here
 				
 				#self.eta = (self.epochs - self.epoch) * self.epoch_time_delta
 				self.epoch_time_deltas = self.epoch_time_deltas + self.epoch_time_delta
@@ -576,14 +569,12 @@ class TrainingState():
 				except Exception as e:
 					pass
 
-				percent = self.epoch / float(self.epochs)
+			if message:
-				message = f'[{self.epoch}/{self.epochs}] [{self.it}/{self.its}] [ETA: {self.eta_hhmmss}] {self.epoch_rate} / {self.it_rate} {self.status}'
+				percent = self.it / float(self.its) # self.epoch / float(self.epochs)
-				
 				if progress is not None:
 					progress(percent, message)
 
-				print(f'{"{:.3f}".format(percent*100)}% {message}')
+				self.buffer.append(f'[{"{:.3f}".format(percent*100)}%] {message}')
-				self.buffer.append(f'{"{:.3f}".format(percent*100)}% {message}')
 
 			if line.find('INFO: [epoch:') >= 0:
 				# easily rip out our stats...
@@ -677,12 +668,36 @@ def convert_to_halfp():
 	torch.save(model, outfile)
 	print(f'Converted model to half precision: {outfile}')
 
+def whisper_transcribe( file, language=None ):
+	# shouldn't happen, but it's for safety
+	if not whisper_model:
+		load_whisper_model(language=language if language else b'en')
+
+	if not args.whisper_cpp:
+		return whisper_model.transcribe(file, language=language if language else "English")
+
+	res = whisper_model.transcribe(file)
+	segments = whisper_model.extract_text_and_timestamps( res )
+
+	result = {
+		'segments': []
+	}
+	for segment in segments:
+		reparsed = {
+			'start': segment[0],
+			'end': segment[1],
+			'text': segment[2],
+		}
+		result['segments'].append(reparsed)
+	return result
+
+
 def prepare_dataset( files, outdir, language=None, progress=None ):
 	unload_tts()
 
 	global whisper_model
 	if whisper_model is None:
-		load_whisper_model()
+		load_whisper_model(language=language)
 
 	os.makedirs(outdir, exist_ok=True)
 
@@ -693,7 +708,7 @@ def prepare_dataset( files, outdir, language=None, progress=None ):
 	for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
 		print(f"Transcribing file: {file}")
 		
-		result = whisper_model.transcribe(file, language=language if language else "English")
+		result = whisper_transcribe(file, language=language) # whisper_model.transcribe(file, language=language if language else "English")
 		results[os.path.basename(file)] = result
 
 		print(f"Transcribed file: {file}, {len(result['segments'])} found.")
@@ -1037,12 +1052,14 @@ def setup_args():
 		'defer-tts-load': False,
 		'device-override': None,
 		'prune-nonfinal-outputs': True,
-		'whisper-model': "base",
-		'autoregressive-model': None,
 		'concurrency-count': 2,
 		'output-sample-rate': 44100,
 		'output-volume': 1,
 		
+		'autoregressive-model': None,
+		'whisper-model': "base",
+		'whisper-cpp': False,
+
 		'training-default-halfp': False,
 		'training-default-bnb': True,
 	}
@@ -1067,13 +1084,15 @@ def setup_args():
 	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("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch")
-	parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
-	parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.")
 	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")
 	parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)")
 	parser.add_argument("--output-volume", type=float, default=default_arguments['output-volume'], help="Adjusts volume of output")
 	
+	parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.")
+	parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
+	parser.add_argument("--whisper-cpp", default=default_arguments['whisper-cpp'], action='store_true', help="Leverages lightmare/whispercpp for transcription")
+	
 	parser.add_argument("--training-default-halfp", action='store_true', default=default_arguments['training-default-halfp'], help="Training default: halfp")
 	parser.add_argument("--training-default-bnb", action='store_true', default=default_arguments['training-default-bnb'], help="Training default: bnb")
 	
@@ -1103,7 +1122,7 @@ 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, 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, output_sample_rate, output_volume, autoregressive_model, whisper_model, whisper_cpp, training_default_halfp, training_default_bnb ):
 	global args
 
 	args.listen = listen
@@ -1123,6 +1142,11 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v
 	args.concurrency_count = concurrency_count
 	args.output_sample_rate = output_sample_rate
 	args.output_volume = output_volume
+	
+	args.autoregressive_model = autoregressive_model
+	args.whisper_model = whisper_model
+	args.whisper_cpp = whisper_cpp
+
 	args.training_default_halfp = training_default_halfp
 	args.training_default_bnb = training_default_bnb
 
@@ -1140,8 +1164,6 @@ def save_args_settings():
 		'defer-tts-load': args.defer_tts_load,
 		'prune-nonfinal-outputs': args.prune_nonfinal_outputs,
 		'device-override': args.device_override,
-		'whisper-model': args.whisper_model,
-		'autoregressive-model': args.autoregressive_model,
 		'sample-batch-size': args.sample_batch_size,
 		'embed-output-metadata': args.embed_output_metadata,
 		'latents-lean-and-mean': args.latents_lean_and_mean,
@@ -1151,6 +1173,10 @@ def save_args_settings():
 		'output-sample-rate': args.output_sample_rate,
 		'output-volume': args.output_volume,
 		
+		'autoregressive-model': args.autoregressive_model,
+		'whisper-model': args.whisper_model,
+		'whisper-cpp': args.whisper_cpp,
+
 		'training-default-halfp': args.training_default_halfp,
 		'training-default-bnb': args.training_default_bnb,
 	}
@@ -1292,9 +1318,7 @@ def update_autoregressive_model(autoregressive_model_path):
 	if not tts:
 		if tts_loading:
 			raise Exception("TTS is still initializing...")
-
+		return
-		load_tts( model=autoregressive_model_path )
-		return # redundant to proceed onward
 
 	print(f"Loading model: {autoregressive_model_path}")
 
@@ -1348,7 +1372,7 @@ def unload_voicefixer():
 
 	do_gc()
 
-def load_whisper_model(name=None, progress=None):
+def load_whisper_model(name=None, progress=None, language=b'en'):
 	global whisper_model
 	
 	if not name:
@@ -1358,7 +1382,12 @@ def load_whisper_model(name=None, progress=None):
 		save_args_settings()
 
 	notify_progress(f"Loading Whisper model: {args.whisper_model}", progress)
-	whisper_model = whisper.load_model(args.whisper_model)
+	if args.whisper_cpp:
+		from whispercpp import Whisper
+		whisper_model = Whisper(name, models_dir='./models/', language=language)
+	else:
+		import whisper
+		whisper_model = whisper.load_model(args.whisper_model)
 	print("Loaded Whisper model")
 
 def unload_whisper():
@@ -1372,10 +1401,13 @@ def unload_whisper():
 
 	do_gc()
 
+"""
 def update_whisper_model(name, progress=None):
 	if not name:
 		return
 
+	args.whisper_model = name
+	save_args_settings()
 
 	global whisper_model
 	if whisper_model:
@@ -1384,3 +1416,4 @@ def update_whisper_model(name, progress=None):
 	else:
 		args.whisper_model = name
 		save_args_settings()
+"""

src/webui.py

@@ -537,7 +537,12 @@ def setup_gradio():
 					
 					autoregressive_models = get_autoregressive_models()
 					autoregressive_model_dropdown = gr.Dropdown(choices=autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else autoregressive_models[0])
+					
 					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)
+					use_whisper_cpp = gr.Checkbox(label="Use Whisper.cpp", value=args.whisper_cpp)
+					
+					exec_inputs = exec_inputs + [ autoregressive_model_dropdown, whisper_model_dropdown, use_whisper_cpp, training_halfp, training_bnb ]
+
 					with gr.Row():
 						autoregressive_models_update_button = gr.Button(value="Refresh Model List")
 						gr.Button(value="Check for Updates").click(check_for_updates)
@@ -559,23 +564,22 @@ def setup_gradio():
 						outputs=autoregressive_model_dropdown,
 					)
 
-					autoregressive_model_dropdown.change(
-						fn=update_autoregressive_model,
-						inputs=autoregressive_model_dropdown,
-						outputs=None
-					)
-					whisper_model_dropdown.change(
-						fn=update_whisper_model,
-						inputs=whisper_model_dropdown,
-						outputs=None
-					)
-
-					exec_inputs = exec_inputs + [ training_halfp, training_bnb ]
-
-
 				for i in exec_inputs:
 					i.change( fn=update_args, inputs=exec_inputs )
 				
+				autoregressive_model_dropdown.change(
+					fn=update_autoregressive_model,
+					inputs=autoregressive_model_dropdown,
+					outputs=None
+				)
+				"""
+				whisper_model_dropdown.change(
+					fn=update_whisper_model,
+					inputs=whisper_model_dropdown,
+					outputs=None
+				)
+				"""
+
 		# console_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
 
 		input_settings = [