whispercpp actually works now (language loading was weird, slicing needed to divide time by 100), transcribing audio checks for silence and discards them

src/utils.py

@@ -39,6 +39,7 @@ from tortoise.utils.device import get_device_name, set_device_name
 MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
 WHISPER_MODELS = ["tiny", "base", "small", "medium", "large"]
 WHISPER_SPECIALIZED_MODELS = ["tiny.en", "base.en", "small.en", "medium.en"]
+EPOCH_SCHEDULE = [ 9, 18, 25, 33 ]
 
 args = None
 tts = None
@@ -997,11 +998,12 @@ def whisper_transcribe( file, language=None ):
 	}
 	for segment in segments:
 		reparsed = {
-			'start': segment[0],
-			'end': segment[1],
+			'start': segment[0] / 100.0,
+			'end': segment[1] / 100.0,
 			'text': segment[2],
 		}
 		result['segments'].append(reparsed)
+
 	return result
 
 
@@ -1014,24 +1016,29 @@ def prepare_dataset( files, outdir, language=None, progress=None ):
 
 	os.makedirs(outdir, exist_ok=True)
 
-	idx = 0
 	results = {}
 	transcription = []
 
 	for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
+		basename = os.path.basename(file)
 		result = whisper_transcribe(file, language=language)
-		results[os.path.basename(file)] = result
+		results[basename] = result
 		print(f"Transcribed file: {file}, {len(result['segments'])} found.")
 
 		waveform, sampling_rate = torchaudio.load(file)
 		num_channels, num_frames = waveform.shape
 
+		idx = 0
 		for segment in result['segments']: # enumerate_progress(result['segments'], desc="Segmenting voice file", progress=progress):
 			start = int(segment['start'] * sampling_rate)
 			end = int(segment['end'] * sampling_rate)
 
 			sliced_waveform = waveform[:, start:end]
-			sliced_name = f"{pad(idx, 4)}.wav"
+			sliced_name = basename.replace(".wav", f"_{pad(idx, 4)}.wav")
+
+			if not torch.any(sliced_waveform < 0):
+				print(f"Error with {sliced_name}, skipping...")
+				continue
 
 			torchaudio.save(f"{outdir}/{sliced_name}", sliced_waveform, sampling_rate)
 
@@ -1056,7 +1063,6 @@ def calc_iterations( epochs, lines, batch_size ):
 	iterations = int(epochs * lines / float(batch_size))
 	return iterations
 
-EPOCH_SCHEDULE = [ 9, 18, 25, 33 ]
 def schedule_learning_rate( iterations, schedule=EPOCH_SCHEDULE ):
 	return [int(iterations * d) for d in schedule]
 
@@ -1750,12 +1756,14 @@ def load_whisper_model(language=None, model_name=None, progress=None):
 		print(f"Loading specialized model for language: {language}")
 
 	notify_progress(f"Loading Whisper model: {model_name}", progress)
+
 	if args.whisper_cpp:
 		from whispercpp import Whisper
 		if not language:
 			language = 'auto'
 
-		whisper_model = Whisper(model_name, models_dir='./models/', language=language.encode('ascii'))
+		b_lang = language.encode('ascii')
+		whisper_model = Whisper(model_name, models_dir='./models/', language=b_lang)
 	else:
 		import whisper
 		whisper_model = whisper.load_model(model_name)