Yes but for example you are normalizing np.linalg.norm(mfccs).
If the tensor is near zero you would be in troubles.
Anyway a good thing you can do is check for NaNs in the loss (it’s a cheap operation).
If there is a NaN you can follow back the error and revise the input or the layers and try to find the reasoin reproducing that
There are very few resons why you would get a NaN.
These are:
A NaN in the weights (propbably due to loss=NaN which was backpropagated)
A operator over an empty tensor.
A normalization step over.