| import sys |
| import numpy as np |
| import ailia.audio as ailia_audio |
|
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| |
| |
| |
| def preemphasis(data, coeff=0.97): |
|
|
| return ailia_audio.linerfilter(np.array([1,-coeff]), np.array([1]), data).astype(np.float32) |
|
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|
|
| def inv_preemphasis(data, coeff=0.97): |
|
|
| return ailia_audio.linerfilter(np.array([1]), np.array([1,-coeff]), data).astype(np.float32) |
|
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|
|
| def lowpass(data, stop_freq, sample_freq, N=4): |
| if( stop_freq == 10000 and sample_freq == 22050) or ( stop_freq == 20000 and sample_freq == 44100): |
| b = np.array([0.68166451, 2.72665802, 4.08998703, 2.72665802, 0.68166451]) |
| a = np.array([1. , 3.238043, 3.99120175, 2.21272074, 0.4646666 ]) |
| else: |
| raise ValueError('illegal sample freqency.') |
| data = ailia_audio.filterfilter(b,a, data) |
|
|
| return data |
|
|
|
|
| def tfconvert(x, window_len, hop_len, mult, window='hann') : |
| y = ailia_audio.spectrogram(x, fft_n=window_len, hop_n=hop_len, center_mode=2, norm_type="scipy",win_type=window) |
|
|
| y_re = np.real(y) * (window_len//2 + 1) |
| y_im = np.imag(y) * (window_len//2 + 1) |
|
|
| y_mag = np.log(np.sqrt(y_re ** 2 + y_im ** 2)+1.0).astype(np.float32) |
| y_phase = np.arctan2(y_im, y_re).astype(np.float32) |
|
|
| y_mag = zero_pad(y_mag, mult) |
| y_phase = zero_pad(y_phase, mult) |
|
|
| return y_mag, y_phase |
|
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|
|
| def zero_pad(x, mult) : |
| mod = x.shape[2] % mult |
| if mod > 0 : |
| pad = mult - mod |
| x = np.concatenate(( x, np.zeros((x.shape[0], x.shape[1], pad), dtype=np.float32) ), axis=2) |
| return x |
|
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|
|
| def calc_time(sample_len ,sr) : |
| quot = sample_len // sr |
| rem = (sample_len % sr) / sr |
| min = quot // 60 |
| sec = quot % 60 + rem |
| print('Time length : {}min {:.02f}sec'.format(min,sec)) |
|
|
