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