actually make parsing VALL-E metrics work

src/utils.py

@@ -686,6 +686,7 @@ class TrainingState():
 		self.statistics = {
 			'loss': [],
 			'lr': [],
+			'grad_norm': [],
 		}
 		self.losses = []
 		self.metrics = {
@@ -696,6 +697,10 @@ class TrainingState():
 
 		self.loss_milestones = [ 1.0, 0.15, 0.05 ]
 
+		if args.tts_backend=="vall-e":
+			self.valle_last_it = 0
+			self.valle_steps = 0
+
 		if keep_x_past_checkpoints > 0:
 			self.cleanup_old(keep=keep_x_past_checkpoints)
 		if start:
@@ -721,6 +726,19 @@ class TrainingState():
 			else:
 				return
 
+		if args.tts_backend == "vall-e":
+			it = data['global_step']
+
+			if self.valle_last_it == it:
+				self.valle_steps += 1
+				return
+			else:
+				self.valle_last_it = it
+				self.valle_steps = 0
+
+			data['it'] = it
+			data['steps'] = self.valle_steps
+
 		self.info = data
 		if 'epoch' in self.info:
 			self.epoch = int(self.info['epoch'])
@@ -755,15 +773,19 @@ class TrainingState():
 			self.metrics['step'].append(f"{self.step}/{self.steps}")
 		self.metrics['step'] = ", ".join(self.metrics['step'])
 
+		if args.tts_backend == "tortoise":
 			epoch = self.epoch + (self.step / self.steps)
+		else:
+			epoch = self.it
 
-		for k in ['lr'] if args.tts_backend == "tortoise" else ['ar.lr', 'nar.lr', 'aar-half.lr', 'nar-half.lr', 'ar-quarter.lr', 'nar-quarter.lr']:
+		if self.it > 0:
+			# probably can double for-loop but whatever
+			for k in ['lr'] if args.tts_backend == "tortoise" else ['ar.lr', 'nar.lr', 'ar-half.lr', 'nar-half.lr', 'ar-quarter.lr', 'nar-quarter.lr']:
 				if k not in self.info:
 					continue
-			
 				self.statistics['lr'].append({'epoch': epoch, 'it': self.it, 'value': self.info[k], 'type': k})
 			
-		for k in ['loss_text_ce', 'loss_mel_ce'] if args.tts_backend == "tortoise" else ['ar.loss', 'nar.loss', 'aar-half.loss', 'nar-half.loss', 'ar-quarter.loss', 'nar-quarter.loss']:
+			for k in ['loss_text_ce', 'loss_mel_ce'] if args.tts_backend == "tortoise" else ['ar.loss', 'nar.loss', 'ar-half.loss', 'nar-half.loss', 'ar-quarter.loss', 'nar-quarter.loss']:
 				if k not in self.info:
 					continue
 
@@ -771,6 +793,11 @@ class TrainingState():
 
 			self.losses.append( self.statistics['loss'][-1] )
 
+			for k in ['ar.grad_norm', 'nar.grad_norm', 'ar-half.grad_norm', 'nar-half.grad_norm', 'ar-quarter.grad_norm', 'nar-quarter.grad_norm']:
+				if k not in self.info:
+					continue
+				self.statistics['grad_norm'].append({'epoch': epoch, 'it': self.it, 'value': self.info[k], 'type': k})
+
 		return data
 
 	def get_status(self):
@@ -862,6 +889,7 @@ class TrainingState():
 		if not update:
 			self.statistics['loss'] = []
 			self.statistics['lr'] = []
+			self.statistics['grad_norm'] = []
 			self.it_rates = 0
 
 		for log in logs:
@@ -869,8 +897,16 @@ class TrainingState():
 				lines = f.readlines()
 
 			for line in lines:
+				line = line.strip()
+				if not line:
+					continue
+					
+				if line[-1] == ".":
+					line = line[:-1]
+
 				if line.find('Training Metrics:') >= 0:
-					data = json.loads(line.split("Training Metrics:")[-1])
+					split = line.split("Training Metrics:")[-1]
+					data = json.loads(split)
 					data['mode'] = "training"
 				elif line.find('Validation Metrics:') >= 0:
 					data = json.loads(line.split("Validation Metrics:")[-1])
@@ -1054,6 +1090,7 @@ def update_training_dataplot(config_path=None):
 	global training_state
 	losses = None
 	lrs = None
+	grad_norms = None
 
 	if not training_state:
 		if config_path:
@@ -1064,6 +1101,8 @@ def update_training_dataplot(config_path=None):
 				losses = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['loss']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Loss Metrics", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
 			if len(training_state.statistics['lr']) > 0:
 				lrs = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['lr']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Learning Rate", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
+			if len(training_state.statistics['grad_norm']) > 0:
+				grad_norms = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['grad_norm']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Gradient Normals", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
 			del training_state
 			training_state = None
 	else:
@@ -1072,8 +1111,10 @@ def update_training_dataplot(config_path=None):
 			losses = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['loss']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Loss Metrics", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
 		if len(training_state.statistics['lr']) > 0:
 			lrs = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['lr']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Learning Rate", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
+		if len(training_state.statistics['grad_norm']) > 0:
+			grad_norms = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['grad_norm']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Gradient Normals", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
 
-	return (losses, lrs)
+	return (losses, lrs, grad_norms)
 
 def reconnect_training(verbose=False, progress=gr.Progress(track_tqdm=True)):
 	global training_state
@@ -2053,10 +2094,8 @@ def get_dataset_list(dir="./training/"):
 def get_training_list(dir="./training/"):
 	if args.tts_backend == "tortoise":
 		return sorted([f'./training/{d}/train.yaml' for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and "train.yaml" in os.listdir(os.path.join(dir, d)) ])
-	
+	else:
-	ars = sorted([f'./training/{d}/ar.yaml' for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and "ar.yaml" in os.listdir(os.path.join(dir, d)) ])
+		return sorted([f'./training/{d}/config.yaml' for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and "config.yaml" in os.listdir(os.path.join(dir, d)) ])
-	nars = sorted([f'./training/{d}/nar.yaml' for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and "nar.yaml" in os.listdir(os.path.join(dir, d)) ])
-	return ars + nars
 
 def pad(num, zeroes):
 	return str(num).zfill(zeroes+1)

src/webui.py

@@ -551,6 +551,16 @@ def setup_gradio():
 							width=500,
 							height=350,
 						)
+						training_grad_norm_graph = gr.LinePlot(label="Training Metrics",
+							x="epoch",
+							y="value",
+							title="Gradient Normals",
+							color="type",
+							tooltip=['epoch', 'it', 'value', 'type'],
+							width=500,
+							height=350,
+							visible=args.tts_backend=="vall-e"
+						)
 						view_losses = gr.Button(value="View Losses")
 		with gr.Tab("Settings"):
 			with gr.Row():
@@ -781,6 +791,7 @@ def setup_gradio():
 			outputs=[
 				training_loss_graph,
 				training_lr_graph,
+				training_grad_norm_graph,
 			],
 			show_progress=False,
 		)
@@ -793,6 +804,7 @@ def setup_gradio():
 			outputs=[
 				training_loss_graph,
 				training_lr_graph,
+				training_grad_norm_graph,
 			],
 		)