| import multiprocessing |
| import os.path |
| from glob import glob |
|
|
| import torch |
| from tqdm import tqdm |
|
|
| from data_conf import data_root |
| from text.bert import get_bert_feature |
| from text.cleaner import clean_text |
| import numpy as np |
| from multiprocessing import Pool |
|
|
| out_dir = "dump" |
| os.makedirs(out_dir, exist_ok=True) |
| phoneme_path = os.path.join(out_dir, "phoneme.npy") |
| phone_dict = {} |
|
|
|
|
| def process_file(data): |
| wav_path, language = data |
| lab_path = wav_path.replace(".wav", ".lab").replace(".mp3", ".lab") |
| if os.path.exists(lab_path): |
| print(lab_path) |
| text = open(lab_path, encoding='utf-8').readline().strip() |
|
|
| try: |
| phones, word2ph, norm_text = clean_text(text, language) |
|
|
| rank = multiprocessing.current_process()._identity |
| rank = rank[0] if len(rank) > 0 else 0 |
| gpu_id = rank % torch.cuda.device_count() |
| device = torch.device(f"cuda:{gpu_id}") |
| bert_feature = get_bert_feature(norm_text, word2ph, device, language) |
| torch.save(bert_feature.cpu(), wav_path.replace(".wav", ".bert.pt").replace(".mp3", ".bert.pt")) |
|
|
| phones = " ".join(phones) |
| return (wav_path, phones) |
| except Exception as e: |
| print(f"Error in {wav_path}, {text}", e) |
| return None |
| else: |
| return None |
|
|
|
|
| if __name__ == '__main__': |
|
|
| for language in ['zh']: |
| filenames = glob(f"{data_root}/{language}/**/*.wav", recursive=True) |
| filenames += glob(f"{data_root}/{language}/**/*.mp3", recursive=True) |
|
|
| |
| num_processes = 1 |
| |
| print(len(filenames)) |
| with Pool(num_processes) as pool: |
| results = list(tqdm(pool.imap(process_file, [(f, language) for f in filenames]), total=len(filenames))) |
|
|
| for result in results: |
| if result is not None: |
| phone_dict[result[0]] = result[1] |
| |
| for k, v in list(phone_dict.items())[:10]: |
| print(k, v) |
| np.save(phoneme_path, phone_dict) |
|
|
