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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
# proposed by @dsmiller
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)
# return int(fsize // 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
# Conversions
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']
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