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

预加重与反预加重

def emphasis(signal, emph_coeff=0.95, pre=True): if pre: result = np.append(signal[0], signal[1:] - emph_coeff * signal[:-1]) else: result = np.append(signal[0], signal[1:] + emph_coeff * signal[:-1])

return result

class SEGAN_Dataset(Dataset):

def __init__(self, para):
    self.file_scp = para.train_scp

    files = np.loadtxt(self.file_scp, dtype='str')
    self.clean_files = files[:, 0].tolist()
    self.noisy_files = files[:, 1].tolist()

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

def __getitem__(self, idx):
    # 读取干净语音并预加重
    clean_wav = np.load(self.clean_files[idx])
    clean_wav = emphasis(clean_wav)
    # 读取含噪语音
    noisy_wav = np.load(self.noisy_files[idx])
    noisy_wav = emphasis(noisy_wav)

    # 读取干净语音并预加重
    clean_wav = torch.from_numpy(clean_wav)
    noisy_wav = torch.from_numpy(noisy_wav)

    # 增加一个维度
    clean_wav = clean_wav.reshape(1, -1)
    noisy_wav = noisy_wav.reshape(1, -1)

    return clean_wav, noisy_wav

def ref_batch(self, batch_size):
    index = np.random.choice(len(self.clean_files), batch_size).tolist()

    catch_clean = [emphasis(np.load(self.clean_files[i])) for i in index]
    catch_noisy = [emphasis(np.load(self.noisy_files[i])) for i in index]
    catch_clean = np.expand_dims(np.array(catch_clean), axis=1)
    catch_noisy = np.expand_dims(np.array(catch_noisy), axis=1)

    batch_wav = np.concatenate((catch_clean, catch_noisy), axis=1)
    return torch.from_numpy(batch_wav)

if name == "main": para = hparams()

m_Dataset = SEGAN_Dataset(para)

m_DataLoader = DataLoader(m_Dataset, batch_size=2, shuffle=True, num_workers=4)

# m_bath_ref = m_Dataset.ref_batch(32)
# print(m_bath_ref.size())

for i_batch, sample_batch in enumerate(m_DataLoader):
    if len(sample_batch[0]) == 1:
        continue
    batch_clean = sample_batch[0]
    batch_noisy = sample_batch[1]
    print(batch_clean[1])
    print(batch_noisy[1])