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I added an initial Parametric UMAP model family + one example model in #688, fixing #631.
I went ahead and merged that in so we could work on other things--there were a lot of changes made to be able to add that model.
There's still additional work to be done though:
fix / better test prep step -- I notice I get a train split that is 0.99 seconds even when I set the target duration to 0.2 seconds, likewise I got a val split that was 0.97 seconds when I set the target duration to 0.1
is this because we are using entire files somehow?
figure out whether we need to shuffle for training -- not clear to me this is needed?
make sure we have access to labels for training and eval when needed
do we need a labelmap.json for this? We're not predicting labels so there's no reason to map <-> consecutive integers
finish predict function
test that vak.predict_.predict calls this function appropriately
modify training dataset in such a way that training doesn't always have to take forever; could we write a custom sampler that uses the probabilities to weight which samples it grabs for each batch?
evaluate the effect of hyperparameters / architecture on the model. To speed up tests I made the default number of filters in each layer of the ConvEncoderUMAP much smaller (in 454f159) and this dropped the checkpoint size from ~1.7GB -> 25MB
The text was updated successfully, but these errors were encountered:
I added an initial Parametric UMAP model family + one example model in #688, fixing #631.
I went ahead and merged that in so we could work on other things--there were a lot of changes made to be able to add that model.
There's still additional work to be done though:
vak.predict_.predict
calls this function appropriatelyThe text was updated successfully, but these errors were encountered: