| import subprocess |
| import matplotlib |
|
|
| matplotlib.use('Agg') |
| import librosa |
| import librosa.filters |
| import numpy as np |
| from scipy import signal |
| from scipy.io import wavfile |
|
|
|
|
| def save_wav(wav, path, sr, norm=False): |
| if norm: |
| wav = wav / np.abs(wav).max() |
| wav *= 32767 |
| |
| wavfile.write(path, sr, wav.astype(np.int16)) |
|
|
|
|
| def get_hop_size(hparams): |
| hop_size = hparams['hop_size'] |
| if hop_size is None: |
| assert hparams['frame_shift_ms'] is not None |
| hop_size = int(hparams['frame_shift_ms'] / 1000 * hparams['audio_sample_rate']) |
| return hop_size |
|
|
|
|
| |
| def _stft(y, hparams): |
| return librosa.stft(y=y, n_fft=hparams['fft_size'], hop_length=get_hop_size(hparams), |
| win_length=hparams['win_size'], pad_mode='constant') |
|
|
|
|
| def _istft(y, hparams): |
| return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams['win_size']) |
|
|
|
|
| def librosa_pad_lr(x, fsize, fshift, pad_sides=1): |
| '''compute right padding (final frame) or both sides padding (first and final frames) |
| ''' |
| assert pad_sides in (1, 2) |
| |
| pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0] |
| if pad_sides == 1: |
| return 0, pad |
| else: |
| return pad // 2, pad // 2 + pad % 2 |
|
|
|
|
| |
| def amp_to_db(x): |
| return 20 * np.log10(np.maximum(1e-5, x)) |
|
|
|
|
| def normalize(S, hparams): |
| return (S - hparams['min_level_db']) / -hparams['min_level_db'] |
|
|
