TIMIT 数据集加载和特征提取 Python 代码

import os
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
from hparams import hparams
import librosa
import random
import soundfile as sf

def feature_stft(wav, para):
    spec = librosa.stft(wav, n_fft=para['N_fft'],
                        win_length=para['win_length'],
                        hop_length=para['hop_length'],
                        window=para['window'])
    mag = np.abs(spec)
    phase = np.angle(spec)

    return mag.T, phase.T  # T x D

# feature T x D

# out T x D*(2*expand+1)

def feature_contex(feature, expend):
    feature = feature.unfold(0, 2 * expend + 1, 1)  # T x D x 2*expand+1
    feature = feature.transpose(1, 2)  # T x 2*n_expand+1 x D
    feature = feature.view([-1, (2 * expend + 1) * feature.shape[-1]])  # T x D * 2*n_expand+1
    # return feature

def get_mask(clean, noisy, para):
    noise = noisy - clean
    clean_mag, _ = feature_stft(clean, para)
    noisy_mag, _ = feature_stft(noisy, para)
    noise_mag, _ = feature_stft(noise, para)
    mask = (clean_mag ** 2 / (clean_mag ** 2 + noise_mag ** 2)) ** (0.5)
    return clean_mag, noisy_mag, mask

class TIMIT_Dataset(Dataset):
    def __init__(self, para):
        self.file_scp = para.file_scp
        self.para_stft = para.para_stft
        self.n_expand = para.n_expand
        files = np.loadtxt(self.file_scp, dtype='str')
        self.clean_files = files[:, 1].tolist()
        self.noisy_files = files[:, 0].tolist()
        print(len(self.clean_files))

    def __len__(self):
        return len(self.clean_files)

    def __getitem__(self, idx):
        # 读取干净语音
        clean_wav, fs = sf.read(self.clean_files[idx], dtype='float32')
        clean_wav = clean_wav.astype('float32')

        # 读取含噪语音

        noisy_wav, fs = sf.read(self.noisy_files[idx], dtype='float32')
        noisy_wav = noisy_wav.astype('float32')

        # 进行 特征提取
        clean_mag, noisy_mag, mask = get_mask(clean_wav, noisy_wav, self.para_stft)

        # 转为torch格式

        X_train = torch.from_numpy(np.log(noisy_mag ** 2))
        Y_train = torch.from_numpy(mask)

        # 拼帧

        X_train = feature_contex(X_train, self.n_expand)
        Y_train = Y_train[self.n_expand:-self.n_expand, :]
        return X_train, Y_train

def my_collect(batch):
    batch_X = [item[0] for item in batch]
    batch_Y = [item[1] for item in batch]
    batch_X = torch.cat(batch_X, 0)
    batch_Y = torch.cat(batch_Y, 0)
    return [batch_X.float(), batch_Y.float()]

if __name__ == '__main__':
    # 数据加载测试
    para = hparams()
    m_Dataset = TIMIT_Dataset(para)
    m_DataLoader = DataLoader(m_Dataset, batch_size=2, shuffle=True, num_workers=4, collate_fn=my_collect)

    for i_batch, sample_batch in enumerate(m_DataLoader):
        train_X = sample_batch[0]
        train_Y = sample_batch[1]
        print(train_X.shape)
        print(train_Y.shape)

# 在哪里输入语音路径内容：语音路径应该在hparams.py中的file_scp参数中进行设置，这个参数指定了一个包含噪语音和干净语音路径的文本文件。每行应该包含两个文件路径，第一个是含噪语音的路径，第二个是对应的干净语音的路径。例如：

noisy1.wav clean1.wav noisy2.wav clean2.wav ...

请注意，这里的路径应该是相对于代码运行的当前工作目录而言的。