since we are overwriting scores.data with an augmented scores.data, just operate on the array in place.

* this results in ~8GB of memory savings on a 9 hour recording

storing ALL calculated masks in a tensor can consume a lot of RAM,
about 4GB for ~9hr audio.
so we save calculating the mask until the loop.
because the mask is now calculated in the loop, we have to wait until after
calculating the mask in order to np.nan_to_num(score)