y, sr = librosa.load('./uploads/{}.wav'.format(language_num))
return(librosa.core.resample(y=y,orig_sr=sr,target_sr=RATE, scale=True))

return(np.array(segments))

segmented_mfccs = []
print('yyy')

for mfcc in X_test:

    segmented_mfccs.append(segment_one(mfcc))
    print('ggg')
return(segmented_mfccs)

#pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())

audio = 'arabic5'
X_test= get_wav(audio)
print(X_test)
X_test=to_mfcc(X_test)

print(X_test)


load_keras_model()
print('hhh')
#X_test = pool.map(get_wav, X_test)
#X_test = pool.map(to_mfcc, X_test)

y_predicted =predict_class_all(create_segmented_mfccs(X_test), model)