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Running
on
Zero
| from librosa.filters import mel as librosa_mel_fn | |
| from torch import nn | |
| from torch.nn import functional as F | |
| import math | |
| import numpy as np | |
| import torch | |
| import torchaudio | |
| def dynamic_range_compression(x, C=1, clip_val=1e-5): | |
| return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) | |
| def dynamic_range_decompression(x, C=1): | |
| return np.exp(x) / C | |
| def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): | |
| return torch.log(torch.clamp(x, min=clip_val) * C) | |
| def dynamic_range_decompression_torch(x, C=1): | |
| return torch.exp(x) / C | |
| def spectral_normalize_torch(magnitudes): | |
| output = dynamic_range_compression_torch(magnitudes) | |
| return output | |
| def spectral_de_normalize_torch(magnitudes): | |
| output = dynamic_range_decompression_torch(magnitudes) | |
| return output | |
| class TorchMelSpectrogram(nn.Module): | |
| def __init__( | |
| self, | |
| filter_length=1024, | |
| hop_length=200, | |
| win_length=800, | |
| n_mel_channels=80, | |
| mel_fmin=0, | |
| mel_fmax=8000, | |
| sampling_rate=16000, | |
| sampling_rate_org=None, | |
| normalize=False, | |
| mel_norm_file=None, | |
| scale=1.0, | |
| padding="center", | |
| style="Tortoise", | |
| ): | |
| super().__init__() | |
| self.style = style | |
| self.filter_length = filter_length | |
| self.hop_length = hop_length | |
| self.win_length = win_length | |
| self.n_mel_channels = n_mel_channels | |
| self.mel_fmin = mel_fmin | |
| self.mel_fmax = mel_fmax | |
| self.sampling_rate = sampling_rate | |
| self.sampling_rate_org = ( | |
| sampling_rate_org if sampling_rate_org is not None else sampling_rate | |
| ) | |
| self.mel_basis = {} | |
| self.hann_window = {} | |
| self.scale = scale | |
| def forward(self, inp, length=None): | |
| if len(inp.shape) == 3: | |
| inp = inp.squeeze(1) if inp.shape[1] == 1 else inp.squeeze(2) | |
| assert len(inp.shape) == 2 | |
| if self.sampling_rate_org != self.sampling_rate: | |
| inp = torchaudio.functional.resample( | |
| inp, self.sampling_rate_org, self.sampling_rate | |
| ) | |
| y = inp | |
| if len(list(self.mel_basis.keys())) == 0: | |
| mel = librosa_mel_fn( | |
| sr=self.sampling_rate, | |
| n_fft=self.filter_length, | |
| n_mels=self.n_mel_channels, | |
| fmin=self.mel_fmin, | |
| fmax=self.mel_fmax, | |
| ) | |
| self.mel_basis[str(self.mel_fmax) + "_" + str(y.device)] = ( | |
| torch.from_numpy(mel).float().to(y.device) | |
| ) | |
| self.hann_window[str(y.device)] = torch.hann_window(self.win_length).to( | |
| y.device | |
| ) | |
| y = torch.nn.functional.pad( | |
| y.unsqueeze(1), | |
| ( | |
| int((self.filter_length - self.hop_length) / 2), | |
| int((self.filter_length - self.hop_length) / 2), | |
| ), | |
| mode="reflect", | |
| ) | |
| y = y.squeeze(1) | |
| # complex tensor as default, then use view_as_real for future pytorch compatibility | |
| spec = torch.stft( | |
| y, | |
| self.filter_length, | |
| hop_length=self.hop_length, | |
| win_length=self.win_length, | |
| window=self.hann_window[str(y.device)], | |
| center=False, | |
| 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[str(self.mel_fmax) + "_" + str(y.device)], spec | |
| ) | |
| spec = spectral_normalize_torch(spec) | |
| max_mel_length = math.ceil(y.shape[-1] / self.hop_length) | |
| spec = spec[..., :max_mel_length].transpose(1, 2) | |
| if length is None: | |
| return spec | |
| else: | |
| spec_len = torch.ceil(length / self.hop_length).clamp(max=spec.shape[1]) | |
| return spec, spec_len | |