| import torch.nn as nn | |
| import torch | |
| import torch.nn.functional as F | |
| import torchaudio | |
| class PreEmphasis(torch.nn.Module): | |
| def __init__(self, coef: float = 0.97): | |
| super().__init__() | |
| self.coef = coef | |
| self.register_buffer( | |
| 'flipped_filter', torch.FloatTensor([-self.coef, 1.]).unsqueeze(0).unsqueeze(0) | |
| ) | |
| def forward(self, input: torch.tensor) -> torch.tensor: | |
| input = input.unsqueeze(1) | |
| input = F.pad(input, (1, 0), 'reflect') | |
| return F.conv1d(input, self.flipped_filter).squeeze(1) | |
| class FbankFeatureExtractor(nn.Module): | |
| """Some Information about MyModule""" | |
| def __init__(self, feat_dim = 80, f_max = 7600, **kwargs): | |
| super(FbankFeatureExtractor, self, ).__init__() | |
| self.torchfbank = torch.nn.Sequential( | |
| PreEmphasis(), | |
| torchaudio.transforms.MelSpectrogram(sample_rate=16000, n_fft=512, win_length=400, hop_length=160, \ | |
| f_min = 20, f_max = f_max, window_fn=torch.hamming_window, n_mels=feat_dim), | |
| ) | |
| self.instance_norm = nn.InstanceNorm1d(feat_dim) | |
| def forward(self, x): | |
| with torch.no_grad(): | |
| x = self.torchfbank(x)+1e-6 | |
| x = x.log() | |
| x = x - torch.mean(x, dim=-1, keepdim=True) | |
| return x |