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| import whisper | |
| import os | |
| import json | |
| import torchaudio | |
| import argparse | |
| import torch | |
| from tqdm import tqdm | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--whisper_size", default="large") | |
| args = parser.parse_args() | |
| #assert (torch.cuda.is_available()), "Please enable GPU in order to run Whisper!" | |
| model = whisper.load_model(args.whisper_size, device="cpu") | |
| parent_dir = "./custom_character_voice/" | |
| speaker_names = list(os.walk(parent_dir))[0][1] | |
| speaker_annos = [] | |
| total_files = sum([len(files) for r, d, files in os.walk(parent_dir)]) | |
| # resample audios | |
| # 2023/4/21: Get the target sampling rate | |
| with open("./configs/amitaro_jp_base.json", 'r', encoding='utf-8') as f: | |
| hps = json.load(f) | |
| target_sr = hps['data']['sampling_rate'] | |
| processed_files = 0 | |
| for speaker in speaker_names: | |
| filelist = (list(os.walk(parent_dir + speaker))[0][2]) | |
| for i, wavfile in tqdm(enumerate(filelist), desc="Processing Audio:", total=len(filelist)): | |
| # try to load file as audio | |
| if wavfile.startswith("processed_"): | |
| continue | |
| #try: | |
| wav, sr = torchaudio.load(parent_dir + speaker + "/" + wavfile, frame_offset=0, num_frames=-1, normalize=True, | |
| channels_first=True) | |
| wav = wav.mean(dim=0).unsqueeze(0) | |
| if sr != target_sr: | |
| wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)(wav) | |
| if wav.shape[1] / sr > 20: | |
| print(f"{wavfile} too long, ignoring\n") | |
| save_path = parent_dir + speaker + "/" + f"processed_{i}.wav" | |
| torchaudio.save(save_path, wav, target_sr, channels_first=True) | |
| # transcribe text | |
| #lang, text = transcribe_one(save_path) | |
| audio = whisper.load_audio(save_path) | |
| audio = whisper.pad_or_trim(audio) | |
| # make log-Mel spectrogram and move to the same device as the model | |
| mel = whisper.log_mel_spectrogram(audio).to(model.device) | |
| options = whisper.DecodingOptions(beam_size=5, language="ja", fp16 = False) | |
| result = whisper.decode(model, mel, options) | |
| text = "[JA]"+ result.text + "[JA]\n" | |
| speaker_annos.append(save_path + "|" + speaker + "|" + text) | |
| processed_files += 1 | |
| #print(f"Processed: {processed_files}/{total_files}") | |
| #except: | |
| # print(f"Error occurred: {wavfile}") | |
| # continue | |
| # # clean annotation | |
| # import argparse | |
| # import text | |
| # from utils import load_filepaths_and_text | |
| # for i, line in enumerate(speaker_annos): | |
| # path, sid, txt = line.split("|") | |
| # cleaned_text = text._clean_text(txt, ["cjke_cleaners2"]) | |
| # cleaned_text += "\n" if not cleaned_text.endswith("\n") else "" | |
| # speaker_annos[i] = path + "|" + sid + "|" + cleaned_text | |
| # write into annotation | |
| if len(speaker_annos) == 0: | |
| print("Warning: no short audios found, this IS expected if you have only uploaded long audios, videos or video links.") | |
| print("this IS NOT expected if you have uploaded a zip file of short audios. Please check your file structure or make sure your audio language is supported.") | |
| with open("short_character_anno.txt", 'w', encoding='utf-8') as f: | |
| for line in speaker_annos: | |
| f.write(line) | |
| # import json | |
| # # generate new config | |
| # with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f: | |
| # hps = json.load(f) | |
| # # modify n_speakers | |
| # hps['data']["n_speakers"] = 1000 + len(speaker2id) | |
| # # add speaker names | |
| # for speaker in speaker_names: | |
| # hps['speakers'][speaker] = speaker2id[speaker] | |
| # # save modified config | |
| # with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f: | |
| # json.dump(hps, f, indent=2) | |
| # print("finished") | |