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import os
import librosa
import librosa.display
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from config import CONFIG
def mkdir_p(mypath):
"""Creates a directory. equivalent to using mkdir -p on the command line"""
from errno import EEXIST
from os import makedirs, path
try:
makedirs(mypath)
except OSError as exc: # Python >2.5
if exc.errno == EEXIST and path.isdir(mypath):
pass
else:
raise
def visualize(target, input, recon, path):
sr = CONFIG.DATA.sr
window_size = 1024
window = np.hanning(window_size)
stft_hr = librosa.core.spectrum.stft(target, n_fft=window_size, hop_length=512, window=window)
stft_hr = 2 * np.abs(stft_hr) / np.sum(window)
stft_lr = librosa.core.spectrum.stft(input, n_fft=window_size, hop_length=512, window=window)
stft_lr = 2 * np.abs(stft_lr) / np.sum(window)
stft_recon = librosa.core.spectrum.stft(recon, n_fft=window_size, hop_length=512, window=window)
stft_recon = 2 * np.abs(stft_recon) / np.sum(window)
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharey=True, sharex=True, figsize=(16, 10))
ax1.title.set_text('Target signal')
ax2.title.set_text('Lossy signal')
ax3.title.set_text('Reconstructed signal')
canvas = FigureCanvas(fig)
p = librosa.display.specshow(librosa.amplitude_to_db(stft_hr), ax=ax1, y_axis='linear', x_axis='time', sr=sr)
p = librosa.display.specshow(librosa.amplitude_to_db(stft_lr), ax=ax2, y_axis='linear', x_axis='time', sr=sr)
p = librosa.display.specshow(librosa.amplitude_to_db(stft_recon), ax=ax3, y_axis='linear', x_axis='time', sr=sr)
mkdir_p(path)
fig.savefig(os.path.join(path, 'spec.png'))
def get_power(x, nfft):
S = librosa.stft(x, n_fft=nfft)
S = np.log(np.abs(S) ** 2 + 1e-8)
return S
def LSD(x_hr, x_pr):
S1 = get_power(x_hr, nfft=2048)
S2 = get_power(x_pr, nfft=2048)
lsd = np.mean(np.sqrt(np.mean((S1 - S2) ** 2 + 1e-8, axis=-1)), axis=0)
S1 = S1[-(len(S1) - 1) // 2:, :]
S2 = S2[-(len(S2) - 1) // 2:, :]
lsd_high = np.mean(np.sqrt(np.mean((S1 - S2) ** 2 + 1e-8, axis=-1)), axis=0)
return lsd, lsd_high
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