import torch import torch.utils.data from scipy.io.wavfile import read from librosa.filters import mel as librosa_mel_fn MAX_WAV_VALUE = 32768.0 def load_wav(full_path): sampling_rate, data = read(full_path) return data, sampling_rate 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, output_energy=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_mel_fn(sampling_rate, n_fft, num_mels, fmin, 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, return_complex=False) spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9)) mel_spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec) mel_spec = _spectral_normalize_torch(mel_spec) if output_energy: energy = torch.norm(spec, dim=1) return mel_spec, energy else: return mel_spec