ttts/prepare/save_mel_vq_to_disk.py

import argparse
import functools
import multiprocessing
import os

import torch
import torchaudio
from tqdm import tqdm
from ttts.classifier.infer import read_jsonl
from ttts.prepare.extract_vq import process_vq
from ttts.utils.utils import find_audio_files, get_paths_with_cache

def extract_vq(file_paths, max_workers):
    with torch.multiprocessing.get_context("spawn").Pool(max_workers) as pool:
        results = list(tqdm(pool.imap(process_vq, file_paths), total=len(file_paths), desc="VQ"))
    # 过滤掉返回None的结果
    results = [result for result in results if result is not None]


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--json_path',default='ttts/datasets/filtered_paths.jsonl')
    args = parser.parse_args()
    # model_path = '/home/hyc/tortoise_plus_zh/ttts/vqvae/logs/2023-11-24-01-21-25/model-30.pt'
    # paths = get_paths_with_cache('ttts/datasets/cliped_datasets','ttts/datasets/wav_clip_path.cache')
    # paths = read_jsonl('ttts/datasets/filtered_paths.jsonl')
    paths = read_jsonl(args.json_path)
    paths = [os.path.join('/home/hyc/tortoise_plus_zh',path['path']) for path in paths]
    num_threads = 8
    extract_vq(paths, num_threads)