move validating audio to creating the text files instead, consider audio longer than 11 seconds invalid, consider text lengths over 200 invalid

src/utils.py

@@ -1065,11 +1065,18 @@ def whisper_transcribe( file, language=None ):
 
 def validate_waveform( waveform, sample_rate ):
 	if not torch.any(waveform < 0):
-		return False
+		return "Waveform is empty"
 
-	if waveform.shape[-1] < (.6 * sample_rate):
+	num_channels, num_frames = waveform.shape
-		return False
+	duration = num_channels * num_frames / sample_rate
-	return True
+	
+	if duration < 0.6:
+		return "Duration too short ({:.3f} < 0.6s)".format(duration)
+
+	if duration > 11:
+		return "Duration too long (11s < {:.3f})".format(duration)
+
+	return
 
 def transcribe_dataset( voice, language=None, skip_existings=False, progress=None ):
 	unload_tts()
@@ -1100,8 +1107,8 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
 		results[basename] = whisper_transcribe(file, language=language)
 
 		# lazy copy
-		waveform, sampling_rate = torchaudio.load(file)
+		waveform, sample_rate = torchaudio.load(file)
-		torchaudio.save(f"{indir}/audio/{basename}", waveform, sampling_rate)
+		torchaudio.save(f"{indir}/audio/{basename}", waveform, sample_rate)
 
 		with open(infile, 'w', encoding="utf-8") as f:
 			f.write(json.dumps(results, indent='\t'))
@@ -1115,6 +1122,7 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
 def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0 ):
 	indir = f'./training/{voice}/'
 	infile = f'{indir}/whisper.json'
+	messages = []
 
 	if not os.path.exists(infile):
 		raise Exception(f"Missing dataset: {infile}")
@@ -1124,15 +1132,21 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0 ):
 	files = 0
 	segments = 0
 	for filename in results:
+		path = f'./voices/{voice}/{filename}'
+		if not os.path.exists(path):
+			path = f'./training/{voice}/{filename}'
+
+		if not os.path.exists(path):
+			messages.append(f"Missing source audio: {filename}")
+			continue
+
 		files += 1
-
 		result = results[filename]
-		waveform, sampling_rate = torchaudio.load(f'./voices/{voice}/{filename}')
+		waveform, sample_rate = torchaudio.load(path)
 
-		for segment in result['segments']: # enumerate_progress(result['segments'], desc="Segmenting voice file", progress=progress):
+		for segment in result['segments']: 
-			segments +=1 
+			start = int((segment['start'] + start_offset) * sample_rate)
-			start = int((segment['start'] + start_offset) * sampling_rate)
+			end = int((segment['end'] + end_offset) * sample_rate)
-			end = int((segment['end'] + end_offset) * sampling_rate)
 
 			if start < 0:
 				start = 0
@@ -1142,20 +1156,19 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0 ):
 			sliced = waveform[:, start:end]
 			file = filename.replace(".wav", f"_{pad(segment['id'], 4)}.wav")
 
-			if not validate_waveform( sliced, sampling_rate ):
-				print(f"Invalid waveform segment ({segment['start']}:{segment['end']}): {file}, skipping...")
-				continue
-
 			if trim_silence:
-				sliced = torchaudio.functional.vad( sliced, sampling_rate )
+				sliced = torchaudio.functional.vad( sliced, sample_rate )
 
-			torchaudio.save(f"{indir}/audio/{file}", sliced, sampling_rate)
+			segments +=1
+			torchaudio.save(f"{indir}/audio/{file}", sliced, sample_rate)
 
-	return f"Sliced segments: {files} => {segments}."
+	messages.append(f"Sliced segments: {files} => {segments}.")
+	return "\n".join(messages)
 
 def prepare_dataset( voice, use_segments, text_length, audio_length ):
 	indir = f'./training/{voice}/'
 	infile = f'{indir}/whisper.json'
+	messages = []
 
 	if not os.path.exists(infile):
 		raise Exception(f"Missing dataset: {infile}")
@@ -1171,16 +1184,27 @@ def prepare_dataset( voice, use_segments, text_length, audio_length ):
 		result = results[filename]
 		segments = result['segments'] if use_segments else [{'text': result['text']}]
 		for segment in segments:
-			text = segment['text'].strip()
 			file = filename.replace(".wav", f"_{pad(segment['id'], 4)}.wav") if use_segments else filename
 			path = f'{indir}/audio/{file}'
 			if not os.path.exists(path):
+				messages.append(f"Missing source audio: {file}")
+				continue
+			
+			text = segment['text'].strip()
+			if len(text) > 200:
+				messages.append(f"[{file}] Text length too long (200 < {len(text)}), skipping...")
+
+			waveform, sample_rate = torchaudio.load(path)
+			num_channels, num_frames = waveform.shape
+			duration = num_channels * num_frames / sample_rate
+
+			error = validate_waveform( waveform, sample_rate )
+			if error:
+				messages.append(f"[{file}]: {error}, skipping...")
 				continue
 
 			culled = len(text) < text_length
 			if not culled and audio_length > 0:
-				metadata = torchaudio.info(path)
-				duration = metadata.num_channels * metadata.num_frames / metadata.sample_rate
 				culled = duration < audio_length
 
 			lines['training' if not culled else 'validation'].append(f'audio/{file}|{text}')
@@ -1194,8 +1218,8 @@ def prepare_dataset( voice, use_segments, text_length, audio_length ):
 	with open(f'{indir}/validation.txt', 'w', encoding="utf-8") as f:
 		f.write(validation_joined)
 
-	msg = f"Prepared {len(lines['training'])} lines (validation: {len(lines['validation'])}).\n{training_joined}\n\n{validation_joined}"
+	messages.append(f"Prepared {len(lines['training'])} lines (validation: {len(lines['validation'])}).\n{training_joined}\n\n{validation_joined}")
-	return msg
+	return "\n".join(messages)
 
 def calc_iterations( epochs, lines, batch_size ):
 	iterations = int(epochs * lines / float(batch_size))
@@ -1213,6 +1237,9 @@ def optimize_training_settings( **kwargs ):
 	with open(dataset_path, 'r', encoding="utf-8") as f:
 		lines = len(f.readlines())
 
+	if lines == 0:
+		raise Exception("Empty dataset.")
+
 	if settings['batch_size'] > lines:
 		settings['batch_size'] = lines
 		messages.append(f"Batch size is larger than your dataset, clamping batch size to: {settings['batch_size']}")	
@@ -1471,17 +1498,17 @@ def import_voices(files, saveAs=None, progress=None):
 			path = f"{outdir}/{os.path.basename(filename)}"
 			print(f"Importing voice to {path}")
 
-			waveform, sampling_rate = torchaudio.load(filename)
+			waveform, sample_rate = torchaudio.load(filename)
 
 			if args.voice_fixer:
 				if not voicefixer:
 					load_voicefixer()
 
 				# resample to best bandwidth since voicefixer will do it anyways through librosa
-				if sampling_rate != 44100:
+				if sample_rate != 44100:
 					print(f"Resampling imported voice sample: {path}")
 					resampler = torchaudio.transforms.Resample(
-						sampling_rate,
+						sample_rate,
 						44100,
 						lowpass_filter_width=16,
 						rolloff=0.85,
@@ -1489,9 +1516,9 @@ def import_voices(files, saveAs=None, progress=None):
 						beta=8.555504641634386,
 					)
 					waveform = resampler(waveform)
-					sampling_rate = 44100
+					sample_rate = 44100
 
-				torchaudio.save(path, waveform, sampling_rate)
+				torchaudio.save(path, waveform, sample_rate)
 
 				print(f"Running 'voicefixer' on voice sample: {path}")
 				voicefixer.restore(
@@ -1501,7 +1528,7 @@ def import_voices(files, saveAs=None, progress=None):
 					#mode=mode,
 				)
 			else:
-				torchaudio.save(path, waveform, sampling_rate)
+				torchaudio.save(path, waveform, sample_rate)
 
 			print(f"Imported voice to {path}")