| import torch | |
| import torchaudio | |
| import librosa | |
| mel_basis = {} | |
| hann_window = {} | |
| 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 | |
| def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): | |
| if torch.min(y) < -1.: | |
| print('min value is ', torch.min(y)) | |
| if torch.max(y) > 1.: | |
| print('max value is ', torch.max(y)) | |
| global mel_basis, hann_window | |
| if fmax not in mel_basis: | |
| mel = librosa.filters.mel(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.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) | |
| 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.numpy() |