from moviepy.editor import AudioFileClip import whisper import os import torchaudio import librosa import torch import argparse parent_dir = "./denoised_audio/" filelist = list(os.walk(parent_dir))[0][2] if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--languages", default="CJE") parser.add_argument("--whisper_size", default="medium") args = parser.parse_args() if args.languages == "CJE": lang2token = { 'zh': "[ZH]", 'ja': "[JA]", "en": "[EN]", } elif args.languages == "CJ": lang2token = { 'zh': "[ZH]", 'ja': "[JA]", } elif args.languages == "C": lang2token = { 'zh': "[ZH]", } assert(torch.cuda.is_available()), "Please enable GPU in order to run Whisper!" model = whisper.load_model(args.whisper_size) speaker_annos = [] for file in filelist: print(f"transcribing {parent_dir + file}...\n") options = dict(beam_size=5, best_of=5) transcribe_options = dict(task="transcribe", **options) result = model.transcribe(parent_dir + file, **transcribe_options) segments = result["segments"] # result = model.transcribe(parent_dir + file) lang = result['language'] if result['language'] not in list(lang2token.keys()): print(f"{lang} not supported, ignoring...\n") continue # segment audio based on segment results character_name = file.rstrip(".wav").split("_")[0] code = file.rstrip(".wav").split("_")[1] if not os.path.exists("./segmented_character_voice/" + character_name): os.mkdir("./segmented_character_voice/" + character_name) wav, sr = torchaudio.load(parent_dir + file, frame_offset=0, num_frames=-1, normalize=True, channels_first=True) for i, seg in enumerate(result['segments']): start_time = seg['start'] end_time = seg['end'] text = seg['text'] text = lang2token[lang] + text.replace("\n", "") + lang2token[lang] text = text + "\n" wav_seg = wav[:, int(start_time*sr):int(end_time*sr)] wav_seg_name = f"{character_name}_{code}_{i}.wav" savepth = "./segmented_character_voice/" + character_name + "/" + wav_seg_name speaker_annos.append(savepth + "|" + character_name + "|" + text) print(f"Transcribed segment: {speaker_annos[-1]}") # trimmed_wav_seg = librosa.effects.trim(wav_seg.squeeze().numpy()) # trimmed_wav_seg = torch.tensor(trimmed_wav_seg[0]).unsqueeze(0) torchaudio.save(savepth, wav_seg, 22050, channels_first=True) if len(speaker_annos) == 0: print("Warning: no long audios & videos found, this IS expected if you have only uploaded short audios") print("this IS NOT expected if you have uploaded any long audios, videos or video links. Please check your file structure or make sure your audio/video language is supported.") with open("long_character_anno.txt", 'w', encoding='utf-8') as f: for line in speaker_annos: f.write(line)