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| import numpy as np | |
| import torch | |
| import torch.utils.data | |
| from librosa.filters import mel as librosa_mel_fn | |
| from scipy.io.wavfile import read | |
| MAX_WAV_VALUE = 32768.0 | |
| def load_wav(full_path): | |
| sampling_rate, data = read(full_path) | |
| return data, sampling_rate | |
| 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 | |
| mel_basis = {} | |
| hann_window = {} | |
| def mel_spectrogram(y, hparams, center=False, complex=False): | |
| # hop_size: 512 # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate) | |
| # win_size: 2048 # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate) | |
| # fmin: 55 # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) | |
| # fmax: 10000 # To be increased/reduced depending on data. | |
| # fft_size: 2048 # Extra window size is filled with 0 paddings to match this parameter | |
| # n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, | |
| n_fft = hparams['fft_size'] | |
| num_mels = hparams['audio_num_mel_bins'] | |
| sampling_rate = hparams['audio_sample_rate'] | |
| hop_size = hparams['hop_size'] | |
| win_size = hparams['win_size'] | |
| fmin = hparams['fmin'] | |
| fmax = hparams['fmax'] | |
| y = y.clamp(min=-1., max=1.) | |
| 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) | |
| if not complex: | |
| 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) | |
| else: | |
| B, C, T, _ = spec.shape | |
| spec = spec.transpose(1, 2) # [B, T, n_fft, 2] | |
| return spec | |