analyze_spectogram.py

from dataset.spectogram.preprocess import multichannel_stft, multichannel_complex_to_log_mel
from dataset.dataset_utils import read_multichannel_audio
from dataset.spectogram import spectogram_configs as cfg
import matplotlib.pyplot as plt
import numpy as np
import soundfile
import matplotlib
matplotlib.use('TkAgg')

if __name__ == '__main__':
    # audio_path = '/home/ariel/projects/sound/data/FilmClap/original/Meron/S005-S004T1.WAV'
    # audio_path = '/home/ariel/projects/sound/data/FilmClap/original/StillJames/2C-T001.WAV'
    # audio_path = '/home/ariel/projects/sound/data/FilmClap/original/JackRinger-05/161019_1233.wav'
    audio_path = '/home/ariel/projects/sound/data/FilmClap/original/StillJames/8D-T001.WAV'

    sec_start = 35.45
    sec_end = 35.65

    multichannel_waveform = read_multichannel_audio(audio_path=audio_path, target_fs=cfg.working_sample_rate)


    multichannel_waveform = multichannel_waveform[int(cfg.working_sample_rate * sec_start): int(cfg.working_sample_rate * sec_end)]
    soundfile.write("tmp_file.WAV", multichannel_waveform, cfg.working_sample_rate)
    feature = multichannel_stft(multichannel_waveform)
    feature = multichannel_complex_to_log_mel(feature)

    frames_num = feature.shape[1]
    tick_hop = max(1, frames_num // 20)
    xticks = np.concatenate((np.arange(0, frames_num - tick_hop, tick_hop), [frames_num]))
    xlabels = [f"{x / cfg.frames_per_second:.3f}s" for x in xticks]

    fig = plt.figure()
    ax = fig.add_subplot(211)
    ax.matshow(feature[0].T, origin='lower', cmap='jet')
    ax.set_xticks(xticks)
    ax.set_xticklabels(xlabels, rotation='vertical')
    ax.xaxis.set_ticks_position('bottom')

    ax = fig.add_subplot(212)
    signal = multichannel_waveform.mean(1)
    ax.plot(range(len(signal)), signal)

    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
    plt.autoscale(tight=True)

    plt.show()