| 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']
|
|
|
