Employing RAVE and vschaos2 neural audio models in larger compositions + seed conservation in arrays

In this video, I'm showcasing the patch and configuration that led to track "Tritt Nochmal Zu" from my Saatgut Proxy/ Saatgut Proxy Reflux release in late 2023/ early 2024. The patch contains a setup of vschaos2 and RAVE model decoder layers and predefined seeds for mocking latent embeddings plus a randomized harmonic pad and a minimal kick drum synthesizer.
The models have been trained on a dataset selected from my own release material.

https://martsman.bandcamp.com/album/s...
https://www.ninaprotocol.com/releases...
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RAVE is "A variational autoencoder for fast and high-quality neural audio synthesis” created by Antoine Caillon and Philippe Esling of Artificial Creative Intelligence and Data Science (ACIDS) at IRCAM, Paris.

vschaos2 is a vintage-flavoured neural audio synthesis package by Axel Chemla Romeu Santos. It is based on unsupervised/ (semi-)supervised training of spectral information using variational auto-encoders.

RAVE on GitHub: https://github.com/acids-ircam/RAVE
nn~ on GitHub: https://github.com/acids-ircam/nn_tilde
vschaos2 on GitHub: https://github.com/acids-ircam/vschaos2

To train models on Colab or Kaggle, you can use these Jupyter notebooks i've set up: https://github.com/devstermarts/Noteb...