data_module.py

import os
 
import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
import numpy as np
import pytorch_lightning as pl
import random
import librosa
from os.path import basename, exists, join
from torch.utils.data import Dataset, DataLoader
import hydra
import utils
import torchaudio
from transformers import AutoFeatureExtractor
from torchaudio.transforms import Resample
from tqdm import tqdm
from torchaudio.transforms import Resample
class DataModule(pl.LightningDataModule):
    def __init__(self, cfg):
        super().__init__()
        self.cfg = cfg
        
        ocwd = hydra.utils.get_original_cwd()
        self.ocwd = ocwd

    def get_loader(self, phase):
        phase_cfg = self.cfg.dataset.get(phase)
        batch_size = phase_cfg.batch_size
        ds = FSDataset(phase, self.cfg)
        # ds = FSDataset_add_STFT(phase, self.cfg)
        dl = DataLoader(ds, 
                        batch_size=batch_size,
                        shuffle=phase_cfg.shuffle,
                        num_workers=28,
                        collate_fn=ds.collate_fn,
                        pin_memory=True,
                        persistent_workers=True)

        return dl

    def train_dataloader(self):
        return self.get_loader('train')

    def val_dataloader(self):
        return self.get_loader('val')

    def test_dataloader(self):
        pass

class FSDataset(Dataset):
    """Dataset batching wav, mel 
    and other acoustic features

    Args:
        phase: train, val, test
        cfg: hydra config
    """
    def __init__(self, phase, cfg):
        self.phase = phase
        self.cfg = cfg
        self.phase_cfg = cfg.dataset.get(phase)
        self.ocwd = hydra.utils.get_original_cwd()
        
        self.sr = cfg.preprocess.audio.sr
        
        # self.filelist = utils.read_filelist(join(self.ocwd, self.phase_cfg.filelist))
        self.filelist = self.get_filelist(self.phase_cfg.filelist)
        self.min_audio_length = cfg.dataset.min_audio_length
        self.feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/w2v-bert-2.0")
    def __len__(self):
        return len(self.filelist)

    def load_wav(self, path):
        wav, sr = librosa.load(path, sr=self.sr)
        return wav

    def get_filelist(self, fpath):
        with open(fpath, 'r') as f:
            # flist = [l.strip() for l in f if l.strip()]
            flist = [l.strip().split('\t')[0] for l in f if l.strip()]
        return flist

    def __getitem__(self, idx):
        # (  wavpath,fid) = self.filelist[idx]
        wavpath  = self.filelist[idx]
        wavpath_full = join(self.cfg.preprocess.datasets.LibriSpeech.root, wavpath)
        # wav = self.load_wav(wavpath)
        # wav = torch.from_numpy(wav)
 
        wav,sr=torchaudio.load(wavpath_full) 
 
                 
        if sr != 16000:
            wav = Resample(sr, 16000)(wav)
        wav = wav[0,:]
        length = wav.shape[0]
        # length = wav.shape[1]
        if length < self.min_audio_length:
            wav = F.pad(wav, (0, self.min_audio_length - length))
            length = wav.shape[0]
        i = random.randint(0, length-self.min_audio_length)
        wav = wav[i:i+self.min_audio_length]

        wav_pad = F.pad(wav, (160, 160))
        feat = self.feature_extractor(wav_pad, sampling_rate=16000, return_tensors="pt") .data['input_features']
        out = {
 
            'wav': wav,
            'feat': feat,
            # 'paths': wavpath_full
        }
        
        return out
    
    def collate_fn(self, bs):
 
        wavs = [b['wav'] for b in bs]
        wavs = torch.stack(wavs)
        feats = [b['feat'] for b in bs]
        feats = torch.stack(feats)
        out = {
 
            'wav': wavs,  
            'feats': feats,
            # 'paths': [b['paths'] for b in bs]
        }
        return out

@hydra.main(config_path='config', config_name='default', version_base=None)
def main(cfg):
 
    # 初始化DataModule
    data_module = DataModule(cfg)

    # 获取训练数据加载器
    train_loader = data_module.val_dataloader()

    # 用于保存无错误的音频路径
    valid_filelist = []

    # 遍历数据集
    for batch_idx, batch in enumerate(tqdm(train_loader, desc="Processing batches", unit="batch")):
        # try:
        wavs = batch['wav']
            # paths = batch['paths']
            # print(f"Loaded batch {batch_idx + 1} with shape: {wavs.shape}")
            
            # 将没有问题的路径保存到 valid_filelist
        #     valid_filelist.extend(paths)
        # except Exception as e:
        #     # 如果遇到问题，打印错误信息和文件路径
        #     print(f"Error in batch {batch_idx + 1}: {e}")
        #     print(f"Paths in this batch: {batch['paths']}")

    # 保存没有问题的音频文件列表到新的文件中
    # with open('/aifs4su/data/zheny/data/data_8_21_2/mls_all_audio_path2.txt', 'w') as f:
    #     for item in valid_filelist:
    #         f.write(f"{item}\n")
        c=1
    print(f"Successfully saved valid filelist to 'valid_filelist.txt'")

if __name__ == "__main__":
    main()