ugh

src/utils.py

@@ -1131,6 +1131,24 @@ def convert_to_halfp():
 	torch.save(model, outfile)
 	print(f'Converted model to half precision: {outfile}')
 
+
+# collapses short segments into the previous segment
+def whisper_sanitize( results ):
+	sanitized = results
+	sanitized['segments'] = []
+
+	for segment in results['segments']:
+		length = segment['end'] - segment['start']
+		if length >= MIN_TRAINING_DURATION or len(sanitized['segments']) == 0:
+			sanitized['segments'].append(segment)
+			continue
+
+		last_segment = sanitized['segments'][-1]
+		last_segment['text'] += segment['text']
+		last_segment['end'] = segment['end']
+
+	return sanitized
+
 def whisper_transcribe( file, language=None ):
 	# shouldn't happen, but it's for safety
 	global whisper_model
@@ -1150,7 +1168,7 @@ def whisper_transcribe( file, language=None ):
 		segments = whisper_model.extract_text_and_timestamps( res )
 
 		result = {
-			'text': []
+			'text': [],
 			'segments': []
 		}
 		for segment in segments:
@@ -1248,6 +1266,8 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
 			result = whisper_transcribe(file, language=language)
 			results[basename] = result
 
+		# results[basename] = whisper_sanitize(results[basename])
+
 		waveform, sample_rate = torchaudio.load(file)
 		# resample to the input rate, since it'll get resampled for training anyways
 		# this should also "help" increase throughput a bit when filling the dataloaders