import torch from multiprocessing import Pool import commons import utils from tqdm import tqdm from text import cleaned_text_to_sequence, get_bert import argparse import torch.multiprocessing as mp def process_line(line): rank = mp.current_process()._identity rank = rank[0] if len(rank) > 0 else 0 if torch.cuda.is_available(): gpu_id = rank % torch.cuda.device_count() device = torch.device(f"cuda:{gpu_id}") wav_path, _, language_str, text, phones, tone, word2ph = line.strip().split("|") phone = phones.split(" ") tone = [int(i) for i in tone.split(" ")] word2ph = [int(i) for i in word2ph.split(" ")] word2ph = [i for i in word2ph] phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) phone = commons.intersperse(phone, 0) tone = commons.intersperse(tone, 0) language = commons.intersperse(language, 0) for i in range(len(word2ph)): word2ph[i] = word2ph[i] * 2 word2ph[0] += 1 bert_path = wav_path.replace(".wav", ".bert.pt") try: bert = torch.load(bert_path) assert bert.shape[-1] == len(phone) except Exception: bert = get_bert(text, word2ph, language_str, device) assert bert.shape[-1] == len(phone) torch.save(bert, bert_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("-c", "--config", type=str, default="configs/config.json") parser.add_argument("--num_processes", type=int, default=2) args = parser.parse_args() config_path = args.config hps = utils.get_hparams_from_file(config_path) lines = [] with open(hps.data.training_files, encoding="utf-8") as f: lines.extend(f.readlines()) with open(hps.data.validation_files, encoding="utf-8") as f: lines.extend(f.readlines()) num_processes = args.num_processes with Pool(processes=num_processes) as pool: for _ in tqdm(pool.imap_unordered(process_line, lines), total=len(lines)): pass