| | import librosa |
| | import torch |
| | import torch.nn as nn |
| | import torch.nn.functional as F |
| |
|
| | class PreEmphasis(torch.nn.Module): |
| | def __init__(self, coef: float = 0.97): |
| | super(PreEmphasis, self).__init__() |
| | self.coef = coef |
| | |
| | |
| | self.register_buffer( |
| | 'flipped_filter', torch.FloatTensor( |
| | [-self.coef, 1.]).unsqueeze(0).unsqueeze(0) |
| | ) |
| |
|
| | def forward(self, inputs: torch.tensor) -> torch.tensor: |
| | assert len( |
| | inputs.size()) == 2, 'The number of dimensions of inputs tensor must be 2!' |
| | |
| | inputs = inputs.unsqueeze(1) |
| | inputs = F.pad(inputs, (1, 0), 'reflect') |
| | return F.conv1d(inputs, self.flipped_filter).squeeze(1) |
| |
|
| |
|
| | class Mel_Spectrogram(nn.Module): |
| | def __init__(self, sample_rate=16000, n_fft=512, win_length=400, hop=160, n_mels=80, coef=0.97, requires_grad=False): |
| | super(Mel_Spectrogram, self).__init__() |
| | self.n_fft = n_fft |
| | self.n_mels = n_mels |
| | self.win_length = win_length |
| | self.hop = hop |
| |
|
| | self.pre_emphasis = PreEmphasis(coef) |
| | mel_basis = librosa.filters.mel( |
| | sr=sample_rate, n_fft=n_fft, n_mels=n_mels) |
| | self.mel_basis = nn.Parameter( |
| | torch.FloatTensor(mel_basis), requires_grad=requires_grad) |
| | self.instance_norm = nn.InstanceNorm1d(num_features=n_mels) |
| | window = torch.hamming_window(self.win_length) |
| | self.window = nn.Parameter( |
| | torch.FloatTensor(window), requires_grad=False) |
| |
|
| | def forward(self, x): |
| | x = self.pre_emphasis(x) |
| | x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, |
| | window=self.window, win_length=self.win_length, return_complex=True) |
| | x = torch.abs(x) |
| | x += 1e-9 |
| | x = torch.log(x) |
| | x = torch.matmul(self.mel_basis, x) |
| | x = self.instance_norm(x) |
| | x = x.permute(0, 2, 1) |
| | x = x.unsqueeze(1) |
| | return x |
