From 853c7fdccf13277e500307aa241e149d7e99ac7d Mon Sep 17 00:00:00 2001
From: mrq <mrq@ecker.tech>
Date: Wed, 3 May 2023 21:31:37 +0000
Subject: [PATCH] forgot to uncomment the block to transcribe and slice when
 using transcribe all because I was piece-processing a huge batch of LibriTTS
 and somehow that leaked over to the repo

---
 src/utils.py | 19 ++++++++++++++++++-
 src/webui.py |  4 +---
 2 files changed, 19 insertions(+), 4 deletions(-)

diff --git a/src/utils.py b/src/utils.py
index 4eb4b85..8e5ea00 100755
--- a/src/utils.py
+++ b/src/utils.py
@@ -1458,6 +1458,7 @@ class TrainingState():
 				'lrs': ['lr'],
 				'losses': ['loss_text_ce', 'loss_mel_ce'],
 				'accuracies': [],
+				'precisions': [],
 				'grad_norms': [],
 			}
 			if args.tts_backend == "vall-e":
@@ -1481,6 +1482,11 @@ class TrainingState():
 					'ar-half.loss.acc', 'nar-half.loss.acc',
 					'ar-quarter.loss.acc', 'nar-quarter.loss.acc',
 				]
+				keys['precisions'] = [
+					'ar.loss.precision', 'nar.loss.precision',
+					'ar-half.loss.precision', 'nar-half.loss.precision',
+					'ar-quarter.loss.precision', 'nar-quarter.loss.precision',
+				]
 				keys['grad_norms'] = ['ar.grad_norm', 'nar.grad_norm', 'ar-half.grad_norm', 'nar-half.grad_norm', 'ar-quarter.grad_norm', 'nar-quarter.grad_norm']
 
 			for k in keys['lrs']:
@@ -1494,6 +1500,12 @@ class TrainingState():
 					continue
 
 				self.statistics['loss'].append({'epoch': epoch, 'it': self.it, 'value': self.info[k], 'type': k})
+
+			for k in keys['precisions']:
+				if k not in self.info:
+					continue
+
+				self.statistics['loss'].append({'epoch': epoch, 'it': self.it, 'value': self.info[k], 'type': k})
 			
 			for k in keys['losses']:
 				if k not in self.info:
@@ -1671,7 +1683,10 @@ class TrainingState():
 						for k in data:
 							if data[k] is None:
 								continue
-							averager['metrics'][k].append( data[k] )
+							if k not in averager['metrics']:
+								averager['metrics'][k] = [ data[k] ]
+							else:
+								averager['metrics'][k].append( data[k] )
 
 					unq[f'{it}_{mode}_{name}'] = averager
 				else:
@@ -1685,6 +1700,8 @@ class TrainingState():
 			if args.tts_backend == "vall-e":
 				stats = unq[it]
 				data = {k: sum(v) / len(v) for k, v in stats['metrics'].items() if k not in blacklist }
+				#data = {k: min(v) for k, v in stats['metrics'].items() if k not in blacklist }
+				#data = {k: max(v) for k, v in stats['metrics'].items() if k not in blacklist }
 				data['name'] = stats['name']
 				data['mode'] = stats['mode']
 				data['steps'] = len(stats['metrics']['it'])
diff --git a/src/webui.py b/src/webui.py
index f2a5ba6..e079675 100755
--- a/src/webui.py
+++ b/src/webui.py
@@ -221,7 +221,6 @@ def prepare_all_datasets( language, validation_text_length, validation_audio_len
 	messages = []
 	voices = get_voice_list()
 
-	"""
 	for voice in voices:
 		print("Processing:", voice)
 		message = transcribe_dataset( voice=voice, language=language, skip_existings=skip_existings, progress=progress )
@@ -232,8 +231,7 @@ def prepare_all_datasets( language, validation_text_length, validation_audio_len
 			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 )