Pretrained sharoubgm model for the Musika system for fast infinite waveform music generation. Introduced in this paper.

You can generate music from this pretrained sharoubgm model using the notebook available here.

This pretrained GAN system consists of a ResNet-style generator and discriminator. During training, stability is controlled by adapting the strength of gradient penalty regularization on-the-fly. The gradient penalty weighting term is contained in switch.npy. The generator is conditioned on a latent coordinate system to produce samples of arbitrary length. The latent representations produced by the generator are then passed to a decoder which converts them into waveform audio. The generator has a context window of about 12 seconds of audio.