I am trying to pre-process data for MLP classifier . I am generating 40 MFCC's and 222 spectral centroids per .wav file

This is my model evaluation code

This is the error I am getting. x_train has all float values but still. each index of x_train is length of 262

# Display model architecture summary 
model.summary()

# Calculate pre-training accuracy 
score = model.evaluate(x_test, y_test, verbose=1)
accuracy = 100*score[1]

print("Pre-training accuracy: %.4f%%" % accuracy)

I have converted both of my features into a list('F[]') this way.

X = np.array(featuresdf.feature_mfcc.tolist() , dtype=object)
# print(type(X))
X2 = np.array(featuresdf.sc.tolist() , dtype=object)
# print(type(X2))
# print(X2)
F = []
for i , val in enumerate(X):
    temp_x = val
    temp_x2 = X2[i]
    
#     concat = temp_x + temp_x2
    concat = np.hstack((temp_x,temp_x2))
    F.append(concat)
    
    
# y dependant variable jo hum predict karenge.
y = np.array(featuresdf.class_label.tolist())
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