leverage the librosa python library to extract a spectrogram

extract_spectrogram.py

import librosa.display, os, gc
import numpy as np
import matplotlib.pyplot as plt

def extract_spectrogram(fname, iname):
    audio, sr = librosa.load(fname, res_type='kaiser_fast')
    S = librosa.feature.melspectrogram(audio, sr=sr, n_mels=128)
    log_S = librosa.power_to_db(S, ref=np.max)
    fig = plt.figure(figsize=[1, 1])
    ax = fig.add_subplot(111)
    fig.subplots_adjust(left=0,right=1,bottom=0,top=1)
    ax.axis("off")
    ax.axis("tight")
    plt.margins(0)
    librosa.display.specshow(log_S, sr=sr)
    fig.savefig(iname, dpi=100, pad_inches=0)
    plt.close(fig)
    plt.close('all')
    del audio, S, log_S, ax, fig
    
samples_folder = "soundscapes/"
images_folder = "images/"
already = os.listdir(images_folder)
d = os.listdir(samples_folder)
for i, f in enumerate(d):
    extract_spectrogram(samples_folder+f, f"{images_folder}/{f.replace('.wav', '.png')}")