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from typing import Literal |
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import torch |
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import torch.nn as nn |
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from librosa.filters import mel as librosa_mel_fn |
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def dynamic_range_compression_torch(x, C=1, clip_val=1e-5, *, norm_fn): |
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return norm_fn(torch.clamp(x, min=clip_val) * C) |
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def spectral_normalize_torch(magnitudes, norm_fn): |
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output = dynamic_range_compression_torch(magnitudes, norm_fn=norm_fn) |
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return output |
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class MelConverter(nn.Module): |
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def __init__( |
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self, |
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*, |
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sampling_rate: float, |
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n_fft: int, |
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num_mels: int, |
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hop_size: int, |
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win_size: int, |
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fmin: float, |
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fmax: float, |
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norm_fn, |
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): |
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super().__init__() |
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self.sampling_rate = sampling_rate |
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self.n_fft = n_fft |
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self.num_mels = num_mels |
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self.hop_size = hop_size |
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self.win_size = win_size |
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self.fmin = fmin |
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self.fmax = fmax |
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self.norm_fn = norm_fn |
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mel = librosa_mel_fn(sr=self.sampling_rate, |
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n_fft=self.n_fft, |
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n_mels=self.num_mels, |
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fmin=self.fmin, |
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fmax=self.fmax) |
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mel_basis = torch.from_numpy(mel).float() |
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hann_window = torch.hann_window(self.win_size) |
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self.register_buffer('mel_basis', mel_basis) |
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self.register_buffer('hann_window', hann_window) |
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@property |
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def device(self): |
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return self.mel_basis.device |
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def forward(self, waveform: torch.Tensor, center: bool = False) -> torch.Tensor: |
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waveform = waveform.clamp(min=-1., max=1.).to(self.device) |
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waveform = torch.nn.functional.pad( |
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waveform.unsqueeze(1), |
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[int((self.n_fft - self.hop_size) / 2), |
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int((self.n_fft - self.hop_size) / 2)], |
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mode='reflect') |
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waveform = waveform.squeeze(1) |
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spec = torch.stft(waveform, |
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self.n_fft, |
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hop_length=self.hop_size, |
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win_length=self.win_size, |
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window=self.hann_window, |
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center=center, |
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pad_mode='reflect', |
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normalized=False, |
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onesided=True, |
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return_complex=True) |
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spec = torch.view_as_real(spec) |
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spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) |
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spec = torch.matmul(self.mel_basis, spec) |
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spec = spectral_normalize_torch(spec, self.norm_fn) |
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return spec |
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def get_mel_converter(mode: Literal['16k', '44k']) -> MelConverter: |
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if mode == '16k': |
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return MelConverter(sampling_rate=16_000, |
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n_fft=1024, |
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num_mels=80, |
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hop_size=256, |
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win_size=1024, |
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fmin=0, |
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fmax=8_000, |
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norm_fn=torch.log10) |
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elif mode == '44k': |
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return MelConverter(sampling_rate=44_100, |
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n_fft=2048, |
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num_mels=128, |
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hop_size=512, |
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win_size=2048, |
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fmin=0, |
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fmax=44100 / 2, |
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norm_fn=torch.log) |
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else: |
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raise ValueError(f'Unknown mode: {mode}') |
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