59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
import torch
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from multiprocessing import Pool
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import commons
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import utils
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from tqdm import tqdm
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from loguru import logger
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from text import cleaned_text_to_sequence, get_bert
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config_path = "configs/config.json"
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hps = utils.get_hparams_from_file(config_path)
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total_gpus = torch.cuda.device_count()
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def process_line(line):
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rank = mp.current_process()._identity
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rank = rank[0] if len(rank) > 0 else 0
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if torch.cuda.is_available():
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gpu_id = rank % torch.cuda.device_count()
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device = torch.device(f"cuda:{gpu_id}")
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logger.info(f"Rank {rank} uses device {device}")
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wav_path, _, language_str, text, phones, tone, word2ph = line.strip().split("|")
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phone = phones.split(" ")
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tone = [int(i) for i in tone.split(" ")]
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word2ph = [int(i) for i in word2ph.split(" ")]
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word2ph = [i for i in word2ph]
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phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
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if hps.data.add_blank:
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phone = commons.intersperse(phone, 0)
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tone = commons.intersperse(tone, 0)
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language = commons.intersperse(language, 0)
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for i in range(len(word2ph)):
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word2ph[i] = word2ph[i] * 2
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word2ph[0] += 1
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bert_path = wav_path.replace(".wav", ".bert.pt")
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try:
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bert = torch.load(bert_path)
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assert bert.shape[-1] == len(phone)
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except Exception:
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bert = get_bert(text, word2ph, language_str,device)
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assert bert.shape[-1] == len(phone)
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torch.save(bert, bert_path)
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if __name__ == "__main__":
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lines = []
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with open(hps.data.training_files, encoding="utf-8") as f:
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lines.extend(f.readlines())
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with open(hps.data.validation_files, encoding="utf-8") as f:
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lines.extend(f.readlines())
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# RTX4090 24GB suitable config, if coom, please decrease the processes number.
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with Pool(processes=6) as pool:
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for _ in tqdm(pool.imap_unordered(process_line, lines), total=len(lines)):
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pass
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