[+] Save results

results/tilt-data.json

@@ -0,0 +1 @@
+{"f": [], "m": []}

src/statistics.py

@@ -281,19 +281,6 @@ def collect_visualize_freq():
     sns.set_theme(style="ticks")
     fig, ax = subplots(figsize=(10, 5))
 
-    print("Pitch")
-    print(calc_col_stats(f_means[:, 0]))
-    print(calc_col_stats(m_means[:, 0]))
-    print("F1")
-    print(calc_col_stats(f_means[:, 1]))
-    print(calc_col_stats(m_means[:, 1]))
-    print("F2")
-    print(calc_col_stats(f_means[:, 2]))
-    print(calc_col_stats(m_means[:, 2]))
-    print("F3")
-    print(calc_col_stats(f_means[:, 3]))
-    print(calc_col_stats(m_means[:, 3]))
-
     df = pd.DataFrame({headers[i]: f_means[:, i] for i in range(4)})
     dm = pd.DataFrame({headers[i]: m_means[:, i] for i in range(4)})
     args = dict(orient='h', scale='width', inner='quartile', linewidth=0.5)
@@ -316,6 +303,10 @@ def collect_visualize_freq():
     sns.despine(fig, ax)
     plt.show()
 
+    # Write JSON
+    data = {val: {'f': f_means[:, i].tolist(), 'm': m_means[:, i].tolist()} for i, val in enumerate(['Pitch', 'F1', 'F2', 'F3'])}
+    Path('results/frequency-data.json').write_text(json.dumps(data), 'utf-8')
+
 
 def collect_visualize_tilt():
     """
@@ -358,9 +349,13 @@ def collect_visualize_tilt():
     sns.despine(fig, ax)
     plt.show()
 
+    # Write JSON
+    data = {'f': f_means.tolist(), 'm': m_means.tolist()}
+    Path('results/tilt-data.json').write_text(json.dumps(data), 'utf-8')
+
 
 if __name__ == '__main__':
-    vox_celeb_dir = Path('C:/Datasets/VoxCeleb1/wav')
+    vox_celeb_dir = Path('../Datasets/VoxCeleb1/wav')
     agab = load_vox_celeb_asab_dict(vox_celeb_dir.joinpath('../vox1_meta.csv'))
 
     ############
@@ -384,7 +379,7 @@ if __name__ == '__main__':
     # call_id_vox_celeb(combine_id_tilt)
 
     # 3. Collect statistics and draw visualizations
-    # collect_visualize_tilt()
+    collect_visualize_tilt()
 
     # print(calculate_freq_info(parselmouth.Sound('../00001.wav')))
     # print(calculate_freq_info(parselmouth.Sound('D:/Downloads/Vowels-Extract-Z-44kHz.flac')))

src/test_fft.py

@@ -1,46 +0,0 @@
-import librosa
-import librosa.display
-import matplotlib.pyplot as plt
-import numpy as np
-
-if __name__ == '__main__':
-    y, sr = librosa.load('../test.wav')
-
-    # Plot waveform
-    plt.plot(y)
-    plt.title('Signal')
-    plt.xlabel('Time (samples)')
-    plt.ylabel('Amplitude')
-    plt.show()
-    plt.clf()
-
-    # Plot frequency domain graph at a single time
-    n_fft = 2048
-    ft = np.abs(librosa.stft(y[:n_fft], hop_length=n_fft + 1))
-
-    plt.plot(ft)
-    plt.title('Spectrum')
-    plt.xlabel('Frequency Bin')
-    plt.ylabel('Amplitude')
-    plt.show()
-    plt.clf()
-
-    # Plot spectrogram
-    spec = np.abs(librosa.stft(y, hop_length=512))
-    spec = librosa.amplitude_to_db(spec, ref=np.max)
-
-    librosa.display.specshow(spec, sr=sr, x_axis='time', y_axis='log')
-    plt.colorbar(format='%+2.0f dB')
-    plt.title('Spectrogram')
-    plt.show()
-    plt.clf()
-
-    # Mel transform
-    mel_spect = librosa.feature.melspectrogram(y=y, sr=sr, n_fft=2048, hop_length=1024)
-    mel_spect = librosa.power_to_db(mel_spect, ref=np.max)
-
-    librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time')
-    plt.title('Mel Spectrogram')
-    plt.colorbar(format='%+2.0f dB')
-    plt.show()
-    plt.clf()