|
|
|
|
|
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 |
|
|
