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| import torch | |
| import torch.nn as nn | |
| from librosa.filters import mel as librosa_mel_fn | |
| class MelSpectrogram(nn.Module): | |
| def __init__(self, | |
| sample_rate: int = 22050, | |
| n_fft: int = 1024, | |
| win_length: int = 1024, | |
| hop_length: int = 256, | |
| n_mels: int = 80, | |
| f_min: float = 0, | |
| f_max: float = 8000.0, | |
| norm: str = 'slaney', | |
| center: bool = False | |
| ): | |
| super().__init__() | |
| self.sample_rate = sample_rate | |
| self.n_fft = n_fft | |
| self.hop_length = hop_length | |
| self.win_length = win_length | |
| self.center = center | |
| self.pad_length = int((n_fft - hop_length)/2) | |
| mel_basis = torch.Tensor(librosa_mel_fn(sr=sample_rate, | |
| n_fft=n_fft, n_mels=n_mels, | |
| fmin=f_min, fmax=f_max, | |
| norm=norm)) | |
| window_fn = torch.hann_window(win_length) | |
| self.register_buffer('mel_basis', mel_basis) | |
| self.register_buffer('window_fn', window_fn) | |
| def forward(self, x): | |
| x_pad = torch.nn.functional.pad( | |
| x, (self.pad_length, self.pad_length), mode='reflect') | |
| spec_lin = torch.stft(x_pad, self.n_fft, | |
| self.hop_length, | |
| self.win_length, | |
| self.window_fn, | |
| center=self.center, | |
| return_complex=True) # [B, F, T] | |
| spec_mag = spec_lin.abs().pow_(2).add_(1e-9).sqrt_() | |
| spec_mel = torch.matmul(self.mel_basis, spec_mag) # [B, mels, T] | |
| return spec_mel | |