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import numpy as np |
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import tensorflow as tf |
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import matplotlib.pyplot as plt |
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import matplotlib |
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import librosa |
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from scipy.io.wavfile import write |
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k = 1e-16 |
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def np_log10(x): |
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numerator = np.log(x + 1e-16) |
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denominator = np.log(10) |
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return numerator / denominator |
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def sigmoid(x): |
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s = 1 / (1 + np.exp(-x)) |
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return s |
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def inv_sigmoid(s): |
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x = np.log((s / (1 - s)) + 1e-16) |
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return x |
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def spc_to_VAE_input(spc): |
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"""Restrict value range from 0 to 1.""" |
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return spc / (1 + spc) |
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def VAE_out_put_to_spc(o): |
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"""Inverse transform of function 'spc_to_VAE_input'.""" |
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return o / (1 - o + k) |
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def denoise(spc): |
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"""Filter back ground noise. (Not used.)""" |
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return np.maximum(0, spc - (2e-5)) |
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hop_length = 256 |
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win_length = 1024 |
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def np_power_to_db(S, amin=1e-16, top_db=80.0): |
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"""Helper method for scaling.""" |
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ref = np.max(S) |
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log_spec = 10.0 * np_log10(np.maximum(amin, S)) |
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log_spec -= 10.0 * np_log10(np.maximum(amin, ref)) |
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log_spec = np.maximum(log_spec, np.max(log_spec) - top_db) |
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return log_spec |
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def show_spc(spc, resolution=(512, 256)): |
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"""Show a spectrogram.""" |
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spc = np.reshape(spc, resolution) |
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magnitude_spectrum = np.abs(spc) |
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log_spectrum = np_power_to_db(magnitude_spectrum) |
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plt.imshow(np.flipud(log_spectrum)) |
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plt.show() |
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def save_results(spectrogram, spectrogram_image_path, waveform_path): |
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"""Save the input 'spectrogram' and its waveform (reconstructed bu Griffin Lim) |
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to path provided by 'spectrogram_image_path' and 'waveform_path'.""" |
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magnitude_spectrum = np.abs(spectrogram) |
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log_spc = np_power_to_db(magnitude_spectrum) |
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log_spc = np.reshape(log_spc, (512, 256)) |
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matplotlib.pyplot.imsave(spectrogram_image_path, log_spc, vmin=-100, vmax=0, |
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origin='lower') |
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abs_spec = np.zeros((513, 256)) |
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abs_spec[:512, :] = abs_spec[:512, :] + np.sqrt(np.reshape(spectrogram, (512, 256))) |
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rec_signal = librosa.griffinlim(abs_spec, n_iter=32, hop_length=256, win_length=1024) |
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write(waveform_path, 16000, rec_signal) |
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