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| 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) | |