| 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 |
|
|
