Class imbalance in one hot encoding for CNN I am building a 2D Convolutional Neural Network for MFCC features for audio classification.

The issue I am facing is that there are 2 classes and huge imbalance between them. One class has 17687 samples while other has 67737 samples. I have done one hot encoding so I have two CNN outputs as [1,0] and [0,1].

From my research it seems that adding class_weights in model.fit only seems to work for  binary classification problems. Is there any way I can assign weights to one hot encoded results?
You might want to consider using metrics such as the f1-score in order to measure how well your model does.

Notice that if, in real life, the inbalance will be of a similar nature, then your model has to account for it anyway.