added showing reported training accuracy and eval/validation metrics to graph

src/utils.py

@@ -726,10 +726,6 @@ class TrainingState():
 			else:
 				return
 
-		if 'elapsed_time' in self.info:
-			self.info['iteration_rate'] = self.info['elapsed_time']
-			del self.info['elapsed_time']
-
 		self.info = data
 		if 'epoch' in self.info:
 			self.epoch = int(self.info['epoch'])
@@ -740,6 +736,9 @@ class TrainingState():
 		if 'steps' in self.info:
 			self.steps = int(self.info['steps'])
 
+		if 'elapsed_time' in self.info:
+			self.info['iteration_rate'] = self.info['elapsed_time']
+			del self.info['elapsed_time']
 
 		if 'iteration_rate' in self.info:
 			it_rate = self.info['iteration_rate']
@@ -772,12 +771,40 @@ class TrainingState():
 
 		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']:
+			keys = {
+				'lrs': ['lr'],
+				'losses': ['loss_text_ce', 'loss_mel_ce'],
+				'accuracy': [],
+			}
+			if args.tts_backend == "vall-e":
+				keys['lrs'] = [
+					'ar.lr', 'nar.lr',
+					'ar-half.lr', 'nar-half.lr',
+					'ar-quarter.lr', 'nar-quarter.lr',
+				]
+				keys['losses'] = [
+					'ar.loss', 'nar.loss',
+					'ar-half.loss', 'nar-half.loss',
+					'ar-quarter.loss', 'nar-quarter.loss',
+
+					'ar.loss.nll', 'nar.loss.nll',
+					'ar-half.loss.nll', 'nar-half.loss.nll',
+					'ar-quarter.loss.nll', 'nar-quarter.loss.nll',
+				]
+
+				keys['accuracies'] = [
+					'ar.acc', 'nar.acc',
+					'ar-half.acc', 'nar-half.acc',
+					'ar-quarter.acc', 'nar-quarter.acc',
+				]
+
+			for k in keys['lrs']:
 				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', 'ar-half.loss', 'nar-half.loss', 'ar-quarter.loss', 'nar-quarter.loss']:
+			for k in keys['losses']:
 				if k not in self.info:
 					continue
 
@@ -905,6 +932,9 @@ class TrainingState():
 				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
+
 				else:
 					continue