How to setup? about wavegan HOT 2 CLOSED

After reading the paper. I see that it will take a while to run. At the rate I'm going probably about two weeks.

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In your example you're using a much more focused set (person talking, drum hits, pianos). What are your thoughts on processing long complex sounds like a song? With the assumption that the model doesn't have to produce anything that would pass as human made.

It is generally much more challenging. I have some examples of processing fairly homogenous classical/jazz piano music on the examples website: http://chrisdonahue.com/wavegan_examples/ . However, content with more timbral variation will be much more challenging. As a very weak analogy, it was much harder to get GANs working on ImageNet than MNIST.

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In your example data you have a directory for test/train/valid. When I try to create that structure it gives me error messages and crashes. As far as I can tell it wants the data_dir to have wav/mp3 files. So does the script break the long wav/mp3 in to slices and then splits those in to train/test/valid sets in memory?

It will split longer files into slices if you configure it to do so. See the data considerations section: https://github.com/chrisdonahue/wavegan#data-considerations

How many epochs/cycles does this script run for? I let it run for a couple of days on my first run and I killed it when I thought I had confirmed it worked.

It will run forever. I usually ended up killing it after a fixed number of steps

I read in one of the other questions I read "you will likely need to reduce the value of dim_mul or train_batch_size to ensure that the model still fits into memory.". The Google DataLab VM instance I'm using has 8CPU/30GB RAM/1xK80 GPU, I'm not getting any complaints/errors about memory. Is it OK to assume that this configuration works, even when generating longer sounds (2/4 seconds).

Yes this is fine as long as it doesn't crash.

Last issue is a technical one. It looks like this script is hard coded to CUDA 9. Any idea on what I'd need to do to get it working on the latest drivers? In DataLab I lose the ability to access Tensorboard when I use an older version of CUDA.

I don't think it's hard coded to use CUDA 9 in any particular way. It should work with Tensorflow 1.14.0 which uses CUDA 10 by default. Though it may spit out a ton of deprecation warnings

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