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Running
on
Zero
| import torch | |
| import pyworld as pw | |
| import numpy as np | |
| import soundfile as sf | |
| import os | |
| from torchaudio.functional import pitch_shift | |
| import librosa | |
| from librosa.filters import mel as librosa_mel_fn | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| 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 MelSpectrogram(nn.Module): | |
| def __init__( | |
| self, | |
| n_fft, | |
| num_mels, | |
| sampling_rate, | |
| hop_size, | |
| win_size, | |
| fmin, | |
| fmax, | |
| center=False, | |
| ): | |
| super(MelSpectrogram, self).__init__() | |
| self.n_fft = n_fft | |
| self.hop_size = hop_size | |
| self.win_size = win_size | |
| self.sampling_rate = sampling_rate | |
| self.num_mels = num_mels | |
| self.fmin = fmin | |
| self.fmax = fmax | |
| self.center = center | |
| mel_basis = {} | |
| hann_window = {} | |
| mel = librosa_mel_fn( | |
| sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax | |
| ) | |
| mel_basis = torch.from_numpy(mel).float() | |
| hann_window = torch.hann_window(win_size) | |
| self.register_buffer("mel_basis", mel_basis) | |
| self.register_buffer("hann_window", hann_window) | |
| def forward(self, y): | |
| y = torch.nn.functional.pad( | |
| y.unsqueeze(1), | |
| ( | |
| int((self.n_fft - self.hop_size) / 2), | |
| int((self.n_fft - self.hop_size) / 2), | |
| ), | |
| mode="reflect", | |
| ) | |
| y = y.squeeze(1) | |
| spec = torch.stft( | |
| y, | |
| self.n_fft, | |
| hop_length=self.hop_size, | |
| win_length=self.win_size, | |
| window=self.hann_window, | |
| center=self.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) | |
| return spec |