| | |
| | from typing import Literal |
| |
|
| | 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): |
| | 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, |
| | n_fft: int, |
| | num_mels: int, |
| | hop_size: int, |
| | win_size: int, |
| | fmin: float, |
| | fmax: float, |
| | norm_fn, |
| | ): |
| | 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 |
| |
|
| |
|
| | def get_mel_converter(mode: Literal['16k', '44k']) -> MelConverter: |
| | if mode == '16k': |
| | return MelConverter(sampling_rate=16_000, |
| | n_fft=1024, |
| | num_mels=80, |
| | hop_size=256, |
| | win_size=1024, |
| | fmin=0, |
| | fmax=8_000, |
| | norm_fn=torch.log10) |
| | elif mode == '44k': |
| | return MelConverter(sampling_rate=44_100, |
| | n_fft=2048, |
| | num_mels=128, |
| | hop_size=512, |
| | win_size=2048, |
| | fmin=0, |
| | fmax=44100 / 2, |
| | norm_fn=torch.log) |
| | else: |
| | raise ValueError(f'Unknown mode: {mode}') |
| |
|
