AI Dub: Latent jamming with RAVE and nn~ in a semi controlled patch environment in Pure Data

In this video, I’m showcasing an exemplary dynamic control and audio setup in Pure Data that allows playing with RAVE models in a mixture of style transfer and direct latent manipulation. The purpose of this setup was to both generate rhythmically repetitive information as well as allowing the model to hallucinate and dissolve this repetitiveness to an extent that produces new patterns. The models used in this video have been trained on a selection of my tracks. I’m using the decoder of a model trained on Martsman tracks with certain more spacious characteristics and the encoder unit of a model trained on my Anthone body of work. Realtime processing is done via the nn~ object. 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. RAVE on GitHub: nn~ on GitHub: To train RAVE models on Colab or Kaggle, you can use these Jupyter notebooks i’ve set up: