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Running
on
Zero
| import numpy as np | |
| import torch | |
| import torchaudio | |
| from librosa.filters import mel as librosa_mel_fn | |
| 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_compression_torch(x, C=1, clip_val=1e-5): | |
| return torch.log(torch.clamp(x, min=clip_val) * C) | |
| def spectral_normalize_torch(magnitudes): | |
| output = dynamic_range_compression_torch(magnitudes) | |
| return output | |
| mel_basis = {} | |
| hann_window = {} | |
| def mel_spectrogram( | |
| y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False | |
| ): | |
| global mel_basis, hann_window # pylint: disable=global-statement | |
| if f"{str(fmax)}_{str(y.device)}" not in mel_basis: | |
| mel = librosa_mel_fn( | |
| sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax | |
| ) | |
| mel_basis[str(fmax) + "_" + str(y.device)] = ( | |
| torch.from_numpy(mel).float().to(y.device) | |
| ) | |
| hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) | |
| y = torch.nn.functional.pad( | |
| y.unsqueeze(1), | |
| (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), | |
| mode="reflect", | |
| ) | |
| y = y.squeeze(1) | |
| spec = torch.view_as_real( | |
| torch.stft( | |
| y, | |
| n_fft, | |
| hop_length=hop_size, | |
| win_length=win_size, | |
| window=hann_window[str(y.device)], | |
| center=center, | |
| pad_mode="reflect", | |
| normalized=False, | |
| onesided=True, | |
| return_complex=True, | |
| ) | |
| ) | |
| spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) | |
| spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec) | |
| spec = spectral_normalize_torch(spec) | |
| return spec | |
| class MelExtractor(object): | |
| def __init__( | |
| self, | |
| num_mels: int = 80, | |
| n_fft: int = 1920, | |
| hop_size: int = 480, | |
| win_size: int = 1920, | |
| sampling_rate: int = 24000, | |
| fmin: int = 0, | |
| fmax: int = 8000, | |
| center: bool = False, | |
| ): | |
| super().__init__() | |
| self.num_mels = num_mels | |
| self.n_fft = n_fft | |
| self.hop_size = hop_size | |
| self.win_size = win_size | |
| self.sampling_rate = sampling_rate | |
| self.fmin = fmin | |
| self.fmax = fmax | |
| self.center = center | |
| def __call__(self, audio: torch.Tensor, audio_sr: int): | |
| """Args: | |
| audio(torch.Tensor): shape (1, t) | |
| Returns: | |
| mel(torch.Tensor): shape (1, num_mels, t') | |
| """ | |
| if audio_sr != self.sampling_rate: | |
| audio = torchaudio.functional.resample( | |
| audio, orig_freq=audio_sr, new_freq=self.sampling_rate | |
| ) | |
| audio_sr = self.sampling_rate | |
| mel = mel_spectrogram( | |
| audio, | |
| self.n_fft, | |
| self.num_mels, | |
| self.sampling_rate, | |
| self.hop_size, | |
| self.win_size, | |
| self.fmin, | |
| self.fmax, | |
| self.center, | |
| ) # (1, num_mels, t) | |
| return mel | |