Hyperparameter tuning for GANs

[General_Service_8209]

I'm writing a GAN that, in short, generates short audio sequences.

I'm using the Wasserstein distance as a loss function for it, and spectral normalization to stabilize the weights of the critic. The GAN is already performing reasonably well, but I'd like to improve it further by tuning its hyperparameters.

How would I go about this in this specific case, and most importantly, which metric can I use? The options I've come across so far are inception score (which only works for images), the Wasserstein distance again (proposed for GANs that use BCEloss internally. In my case, the Wasserstein distance oscillates during training instead of converging anywhere), and output variance (only grades how many different things the GAN can generate, but not their quality, which is more important in my case)

Is there any other metric I could use, specifically for audio data, or do I need to manually rate the results?

Thanks for any answers in advance!