short_audio_transcribe.py

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")