a bunch of shit i had uncommited over the past while pertaining to VALL-E

modules/tortoise-tts

@@ -1 +1 @@
-Subproject commit 0bcdf81d0444218b4dedaefa5c546d42f36b8130
+Subproject commit f025470d60fd18993caaa651e6faa585bcc420f0

src/utils.py

@@ -75,6 +75,7 @@ try:
 
 	VALLE_ENABLED = True
 except Exception as e:
+	print(e)
 	pass
 
 if VALLE_ENABLED:
@@ -156,10 +157,12 @@ def generate_valle(**kwargs):
 
 	voice_cache = {}
 	def fetch_voice( voice ):
+		voice_dir = f'./training/{voice}/audio/'
+		if not os.path.isdir(voice_dir):
 			voice_dir = f'./voices/{voice}/'
 		files = [ f'{voice_dir}/{d}' for d in os.listdir(voice_dir) if d[-4:] == ".wav" ]
-		return files
+		# return files
-		# return random.choice(files)
+		return random.choice(files)
 
 	def get_settings( override=None ):
 		settings = {
@@ -1089,13 +1092,13 @@ class TrainingState():
 					'ar-quarter.lr', 'nar-quarter.lr',
 				]
 				keys['losses'] = [
-					'ar.loss', 'nar.loss',
+					'ar.loss', 'nar.loss', 'ar+nar.loss',
-					'ar-half.loss', 'nar-half.loss',
+					'ar-half.loss', 'nar-half.loss', 'ar-half+nar-half.loss',
-					'ar-quarter.loss', 'nar-quarter.loss',
+					'ar-quarter.loss', 'nar-quarter.loss', 'ar-quarter+nar-quarter.loss',
 
-					'ar.loss.nll', 'nar.loss.nll',
+				#	'ar.loss.nll', 'nar.loss.nll',
-					'ar-half.loss.nll', 'nar-half.loss.nll',
+				#	'ar-half.loss.nll', 'nar-half.loss.nll',
-					'ar-quarter.loss.nll', 'nar-quarter.loss.nll',
+				#	'ar-quarter.loss.nll', 'nar-quarter.loss.nll',
 				]
 
 				keys['accuracies'] = [
@@ -1123,7 +1126,7 @@ class TrainingState():
 
 				prefix = ""
 
-				if data["mode"] == "validation":
+				if "mode" in self.info and self.info["mode"] == "validation":
 					prefix = f'{self.info["name"] if "name" in self.info else "val"}_'
 
 				self.statistics['loss'].append({'epoch': epoch, 'it': self.it, 'value': self.info[k], 'type': f'{prefix}{k}' })
@@ -1231,6 +1234,7 @@ class TrainingState():
 
 		unq = {}
 		averager = None
+		prev_state = 0
 
 		for log in logs:
 			with open(log, 'r', encoding="utf-8") as f:
@@ -1250,6 +1254,7 @@ class TrainingState():
 					
 					name = "train"
 					mode = "training"
+					prev_state = 0
 				elif line.find('Validation Metrics:') >= 0:
 					data = json.loads(line.split("Validation Metrics:")[-1])
 					if "it" not in data:
@@ -1257,8 +1262,15 @@ class TrainingState():
 					if "epoch" not in data:
 						data['epoch'] = epoch
 
-					name = data['name'] if 'name' in data else "val"
+					# name = data['name'] if 'name' in data else "val"
 					mode = "validation"
+
+					if prev_state == 0:
+						name = "subtrain"
+					else:
+						name = "val"
+
+					prev_state += 1
 				else:
 					continue
 
@@ -1272,6 +1284,7 @@ class TrainingState():
 					if not averager or averager['key'] != f'{it}_{name}' or averager['mode'] != mode:
 						averager = {
 							'key': f'{it}_{name}',
+							'name': name,
 							'mode': mode,
 							"metrics": {}
 						}
@@ -1292,11 +1305,13 @@ class TrainingState():
 				if update and it <= self.last_info_check_at:
 					continue
 		
+		blacklist = [ "batch", "eval" ]
 		for it in unq:
 			if args.tts_backend == "vall-e":
 				stats = unq[it]
-				data = {k: sum(v) / len(v) for k, v in stats['metrics'].items()}
+				data = {k: sum(v) / len(v) for k, v in stats['metrics'].items() if k not in blacklist }
-				data['mode'] = stats
+				data['name'] = stats['name']
+				data['mode'] = stats['mode']
 				data['steps'] = len(stats['metrics']['it'])
 			else:
 				data = unq[it]
@@ -1633,6 +1648,7 @@ def whisper_transcribe( file, language=None ):
 
 		device = "cuda" if get_device_name() == "cuda" else "cpu"
 		if whisper_vad:
+			# omits a considerable amount of the end
 			"""
 			if args.whisper_batchsize > 1:
 				result = whisperx.transcribe_with_vad_parallel(whisper_model, file, whisper_vad, batch_size=args.whisper_batchsize, language=language, task="transcribe")
@@ -1778,7 +1794,9 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
 	messages = []
 
 	if not os.path.exists(infile):
-		raise Exception(f"Missing dataset: {infile}")
+		message = f"Missing dataset: {infile}"
+		print(message)
+		return message
 
 	if results is None:
 		results = json.load(open(infile, 'r', encoding="utf-8"))
@@ -1903,7 +1921,9 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
 	indir = f'./training/{voice}/'
 	infile = f'{indir}/whisper.json'
 	if not os.path.exists(infile):
-		raise Exception(f"Missing dataset: {infile}")
+		message = f"Missing dataset: {infile}"
+		print(message)
+		return message
 
 	results = json.load(open(infile, 'r', encoding="utf-8"))
 

src/webui.py

@@ -196,6 +196,50 @@ def read_generate_settings_proxy(file, saveAs='.temp'):
 def slice_dataset_proxy( voice, trim_silence, start_offset, end_offset, progress=gr.Progress(track_tqdm=True) ):
 	return slice_dataset( voice, trim_silence=trim_silence, start_offset=start_offset, end_offset=end_offset, results=None, progress=progress )
 
+def diarize_dataset( voice, progress=gr.Progress(track_tqdm=False) ):
+	from pyannote.audio import Pipeline
+	pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=args.hf_token)
+
+	messages = []
+	files = sorted( get_voices(load_latents=False)[voice] )
+	for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
+		diarization = pipeline(file)
+		for turn, _, speaker in diarization.itertracks(yield_label=True):
+			message = f"start={turn.start:.1f}s stop={turn.end:.1f}s speaker_{speaker}"
+			print(message)
+			messages.append(message)
+
+	return "\n".join(messages)
+
+def prepare_all_datasets( language, validation_text_length, validation_audio_length, skip_existings, slice_audio, trim_silence, slice_start_offset, slice_end_offset, progress=gr.Progress(track_tqdm=False) ):
+	kwargs = locals()
+
+	messages = []
+	voices = get_voice_list()
+
+	"""
+	for voice in voices:
+		message = prepare_dataset_proxy(voice, **kwargs)
+		messages.append(message)
+	"""
+	for voice in voices:
+		print("Processing:", voice)
+		message = transcribe_dataset( voice=voice, language=language, skip_existings=skip_existings, progress=progress )
+		messages.append(message)
+
+	if slice_audio:
+		for voice in voices:
+			print("Processing:", voice)
+			message = slice_dataset( voice, trim_silence=trim_silence, start_offset=slice_start_offset, end_offset=slice_end_offset, results=None, progress=progress )
+			messages.append(message)
+
+	for voice in voices:
+		print("Processing:", voice)
+		message = prepare_dataset( voice, use_segments=slice_audio, text_length=validation_text_length, audio_length=validation_audio_length, progress=progress )
+		messages.append(message)
+
+	return "\n".join(messages)
+
 def prepare_dataset_proxy( voice, language, validation_text_length, validation_audio_length, skip_existings, slice_audio, trim_silence, slice_start_offset, slice_end_offset, progress=gr.Progress(track_tqdm=False) ):
 	messages = []
 	
@@ -468,6 +512,8 @@ def setup_gradio():
 							DATASET_SETTINGS['slice_end_offset'] = gr.Number(label="Slice End Offset", value=0)
 
 						transcribe_button = gr.Button(value="Transcribe and Process")
+						transcribe_all_button = gr.Button(value="Transcribe All")
+						diarize_button = gr.Button(value="Diarize")
 						
 						with gr.Row():
 							slice_dataset_button = gr.Button(value="(Re)Slice Audio")
@@ -579,7 +625,7 @@ def setup_gradio():
 							tooltip=['epoch', 'it', 'value', 'type'],
 							width=500,
 							height=350,
-							visible=args.tts_backend=="vall-e"
+							visible=False, # args.tts_backend=="vall-e"
 						)
 						view_losses = gr.Button(value="View Losses")
 
@@ -611,10 +657,7 @@ def setup_gradio():
 					# 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"):
-					default_valle_model_choice = ""
+					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])
-					if len(valle_models):
-						default_valle_model_choice = valle_models[0]
-					EXEC_SETTINGS['valle_model'] = gr.Dropdown(choices=valle_models, label="VALL-E Model Config", value=args.valle_model if args.valle_model else default_valle_model_choice)
 
 				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")
@@ -859,6 +902,16 @@ def setup_gradio():
 			inputs=dataset_settings,
 			outputs=prepare_dataset_output #console_output
 		)
+		transcribe_all_button.click(
+			prepare_all_datasets,
+			inputs=dataset_settings[1:],
+			outputs=prepare_dataset_output #console_output
+		)
+		diarize_button.click(
+			diarize_dataset,
+			inputs=dataset_settings[0],
+			outputs=prepare_dataset_output #console_output
+		)
 		prepare_dataset_button.click(
 			prepare_dataset,
 			inputs=[