my sanitizer actually did work, it was just batch sizes leading to problems when transcribing

src/utils.py

@@ -1206,10 +1206,13 @@ def whisper_transcribe( file, language=None ):
 
 		device = "cuda" if get_device_name() == "cuda" else "cpu"
 		if whisper_vad:
+			"""
 			if args.whisper_batchsize > 1:
 				result = whisperx.transcribe_with_vad_parallel(whisper_model, file, whisper_vad, batch_size=args.whisper_batchsize, language=language, task="transcribe")
 			else:
 				result = whisperx.transcribe_with_vad(whisper_model, file, whisper_vad)
+			"""
+			result = whisperx.transcribe_with_vad(whisper_model, file, whisper_vad)
 		else:
 			result = whisper_model.transcribe(file)
 			
@@ -1282,30 +1285,17 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
 	for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
 		basename = os.path.basename(file)
 
-		modified = False
 		if basename in results and skip_existings:
 			print(f"Skipping already parsed file: {basename}")
-		else:
-			try:
-				result = whisper_transcribe(file, language=language)
-				modified = True
-			except Exception as e:
-				print("Failed to transcribe:", file)
-				continue
-			results[basename] = result
+			continue
 
-		"""
 		try:
-			sanitized = whisper_sanitize(results[basename])
-			if len(sanitized['segments']) > 0 and len(sanitized['segments']) != len(results[basename]['segments']):
-				results[basename] = sanitized
-				modified = True
-				print("Segments sanizited: ", basename)
+			result = whisper_transcribe(file, language=language)
 		except Exception as e:
-			print("Failed to sanitize:", basename, e)
-			pass
-		"""
+			print("Failed to transcribe:", file)
+			continue
 
+		results[basename] = result
 		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
@@ -1314,12 +1304,28 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
 			waveform = waveform[:1]
 		torchaudio.save(f"{indir}/audio/{basename}", waveform, sample_rate, encoding="PCM_S", bits_per_sample=16)
 
-		if modified:
-			with open(infile, 'w', encoding="utf-8") as f:
-				f.write(json.dumps(results, indent='\t'))
+		with open(infile, 'w', encoding="utf-8") as f:
+			f.write(json.dumps(results, indent='\t'))
 
 		do_gc()
 
+	modified = False
+	for basename in results:
+		try:
+			sanitized = whisper_sanitize(results[basename])
+			if len(sanitized['segments']) > 0 and len(sanitized['segments']) != len(results[basename]['segments']):
+				results[basename] = sanitized
+				modified = True
+				print("Segments sanizited: ", basename)
+		except Exception as e:
+			print("Failed to sanitize:", basename, e)
+			pass
+
+	if modified:
+		os.rename(infile, infile.replace(".json", ".unsanitized.json"))
+		with open(infile, 'w', encoding="utf-8") as f:
+			f.write(json.dumps(results, indent='\t'))
+
 	return f"Processed dataset to: {indir}"
 
 def slice_waveform( waveform, sample_rate, start, end, trim ):