Why librosa.load() Runs Out of Memory when generating spectrograms? - python-3.x

Question

I am working on classifying sounds with Deep Learning and my problem is that I run out of memory when I try to convert .wav files to spectrograms using lib.load() from librosa.
split = ['train','val']
categories=['URTI', 'Healthy', 'Asthma', 'COPD', 'LRTI', 'Bronchiectasis','Pneumonia', 'Bronchiolitis']
files_loc = "path"
i=0
for s in split:
for cat in categories:
print('-' * 100)
print('working on ' + cat +" "+str(s)+" "+ '...')
print('-' * 100)
files = [f for f in listdir(files_loc + s + '/' + cat + '/') if isfile(join(files_loc + s + '/' + cat + '/', f)) and is_wav(f)]
for f in files:
convert_to_spec_image(file_loc = files_loc, category=cat, filename=f, is_train=(s == 'train'), verbose=False)
i=i+1
print("We have processed: "+str(i)+" "+ str((i/773*100))+" % "+" so far")
The function convert_to_spec_image is this:
#create images using librosa spectogram
def convert_to_spec_image(file_loc, filename, category, is_train=False, verbose=False):
'''
Converts audio file to spec image
Input file includes path
Saves the file to a png image in the save_directory
'''
train_ = 'train/'
val_ = 'val/'
loc = file_loc + train_ + category + '/' + filename
if is_train == False:
loc = file_loc + val_ + category + '/' + filename
if verbose == True:
print('reading and converting ' + filename + '...')
y, sr = lb.load(loc)
#Plot signal in
plt.figure(figsize=(10,3))
src_ft = lb.stft(y)
src_db = lb.amplitude_to_db(abs(src_ft))
specshow(src_db, sr=sr, x_axis='time', y_axis='hz')
plt.ylim(0, 5000)
save_directory = "C:/Users/raulf/Desktop/espectograms2/"
filename_img = filename.split('.wav')[0]
save_loc = save_directory + train_ + category + '/' + filename_img + '.png'
if is_train == False:
save_loc = save_directory + val_ + category + '/' + filename_img + '.png'
plt.savefig(save_loc)
plt.close('all')
if verbose == True:
print(filename + ' converted!')
plt.close('all')
I am trying to reuse the code from this Kaggle Notebook:
https://www.kaggle.com/danaelisanicolas/cnn-part-3-create-spectrogram-images
Thanks in advance