| from torchaudio import transforms | |
| import random | |
| import torch, torch.nn as nn | |
| class logFbankCal(nn.Module): | |
| def __init__(self, sample_rate, n_fft, win_length, hop_length, n_mels): | |
| super(logFbankCal, self).__init__() | |
| self.fbankCal = transforms.MelSpectrogram(sample_rate=sample_rate, | |
| n_fft=n_fft, | |
| win_length=int(win_length*sample_rate), | |
| hop_length=int(hop_length*sample_rate), | |
| n_mels=n_mels) | |
| def forward(self, x): | |
| out = self.fbankCal(x) | |
| out = torch.log(out + 1e-6) | |
| out = out - out.mean(axis=2).unsqueeze(dim=2) | |
| return out |