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import os |
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import librosa |
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import librosa.display |
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import matplotlib.pyplot as plt |
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import numpy as np |
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from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas |
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from config import CONFIG |
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def mkdir_p(mypath): |
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"""Creates a directory. equivalent to using mkdir -p on the command line""" |
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from errno import EEXIST |
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from os import makedirs, path |
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try: |
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makedirs(mypath) |
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except OSError as exc: |
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if exc.errno == EEXIST and path.isdir(mypath): |
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pass |
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else: |
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raise |
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def visualize(target, input, recon, path): |
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sr = CONFIG.DATA.sr |
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window_size = 1024 |
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window = np.hanning(window_size) |
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stft_hr = librosa.core.spectrum.stft(target, n_fft=window_size, hop_length=512, window=window) |
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stft_hr = 2 * np.abs(stft_hr) / np.sum(window) |
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stft_lr = librosa.core.spectrum.stft(input, n_fft=window_size, hop_length=512, window=window) |
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stft_lr = 2 * np.abs(stft_lr) / np.sum(window) |
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stft_recon = librosa.core.spectrum.stft(recon, n_fft=window_size, hop_length=512, window=window) |
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stft_recon = 2 * np.abs(stft_recon) / np.sum(window) |
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fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharey=True, sharex=True, figsize=(16, 10)) |
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ax1.title.set_text('Target signal') |
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ax2.title.set_text('Lossy signal') |
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ax3.title.set_text('Reconstructed signal') |
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canvas = FigureCanvas(fig) |
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p = librosa.display.specshow(librosa.amplitude_to_db(stft_hr), ax=ax1, y_axis='linear', x_axis='time', sr=sr) |
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p = librosa.display.specshow(librosa.amplitude_to_db(stft_lr), ax=ax2, y_axis='linear', x_axis='time', sr=sr) |
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p = librosa.display.specshow(librosa.amplitude_to_db(stft_recon), ax=ax3, y_axis='linear', x_axis='time', sr=sr) |
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mkdir_p(path) |
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fig.savefig(os.path.join(path, 'spec.png')) |
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def get_power(x, nfft): |
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S = librosa.stft(x, n_fft=nfft) |
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S = np.log(np.abs(S) ** 2 + 1e-8) |
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return S |
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def LSD(x_hr, x_pr): |
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S1 = get_power(x_hr, nfft=2048) |
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S2 = get_power(x_pr, nfft=2048) |
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lsd = np.mean(np.sqrt(np.mean((S1 - S2) ** 2 + 1e-8, axis=-1)), axis=0) |
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S1 = S1[-(len(S1) - 1) // 2:, :] |
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S2 = S2[-(len(S2) - 1) // 2:, :] |
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lsd_high = np.mean(np.sqrt(np.mean((S1 - S2) ** 2 + 1e-8, axis=-1)), axis=0) |
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return lsd, lsd_high |
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