| |
|
|
| import torch |
| import torch.nn as nn |
| from librosa.filters import mel as librosa_mel_fn |
|
|
|
|
| def dynamic_range_compression_torch(x, C=1, clip_val=1e-5, norm_fn=torch.log10): |
| return norm_fn(torch.clamp(x, min=clip_val) * C) |
|
|
|
|
| def spectral_normalize_torch(magnitudes, norm_fn): |
| output = dynamic_range_compression_torch(magnitudes, norm_fn=norm_fn) |
| return output |
|
|
|
|
| class MelConverter(nn.Module): |
|
|
| def __init__( |
| self, |
| *, |
| sampling_rate: float = 16_000, |
| n_fft: int = 1024, |
| num_mels: int = 80, |
| hop_size: int = 256, |
| win_size: int = 1024, |
| fmin: float = 0, |
| fmax: float = 8_000, |
| norm_fn=torch.log10, |
| ): |
| super().__init__() |
| self.sampling_rate = sampling_rate |
| self.n_fft = n_fft |
| self.num_mels = num_mels |
| self.hop_size = hop_size |
| self.win_size = win_size |
| self.fmin = fmin |
| self.fmax = fmax |
| self.norm_fn = norm_fn |
|
|
| mel = librosa_mel_fn(sr=self.sampling_rate, |
| n_fft=self.n_fft, |
| n_mels=self.num_mels, |
| fmin=self.fmin, |
| fmax=self.fmax) |
| mel_basis = torch.from_numpy(mel).float() |
| hann_window = torch.hann_window(self.win_size) |
|
|
| self.register_buffer('mel_basis', mel_basis) |
| self.register_buffer('hann_window', hann_window) |
|
|
| @property |
| def device(self): |
| return self.mel_basis.device |
|
|
| def forward(self, waveform: torch.Tensor, center: bool = False) -> torch.Tensor: |
| waveform = waveform.clamp(min=-1., max=1.).to(self.device) |
|
|
| waveform = torch.nn.functional.pad( |
| waveform.unsqueeze(1), |
| [int((self.n_fft - self.hop_size) / 2), |
| int((self.n_fft - self.hop_size) / 2)], |
| mode='reflect') |
| waveform = waveform.squeeze(1) |
|
|
| spec = torch.stft(waveform, |
| self.n_fft, |
| hop_length=self.hop_size, |
| win_length=self.win_size, |
| window=self.hann_window, |
| center=center, |
| pad_mode='reflect', |
| normalized=False, |
| onesided=True, |
| return_complex=True) |
|
|
| spec = torch.view_as_real(spec) |
| spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) |
| spec = torch.matmul(self.mel_basis, spec) |
| spec = spectral_normalize_torch(spec, self.norm_fn) |
|
|
| return spec |
|
|
