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| # Reference: # https://github.com/bytedance/Make-An-Audio-2 | |
| 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) | |
| 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}') | |