<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Resources-and-Examples :: Creative Python Resources</title><link>https://dolicaakelloegwel.github.io/resources-and-examples/index.html</link><description/><generator>Hugo</generator><language>en-gb</language><atom:link href="https://dolicaakelloegwel.github.io/resources-and-examples/index.xml" rel="self" type="application/rss+xml"/><item><title>Rave Training</title><link>https://dolicaakelloegwel.github.io/resources-and-examples/rave-training/index.html</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://dolicaakelloegwel.github.io/resources-and-examples/rave-training/index.html</guid><description>RAVE is a tool that allows for real-time audio generation. This is guidance on how to setup a Python environment that is suitable for training a RAVE model.&#10;A Linux machine is required for training. However, once your training is complete, it should be possible to export a model that may be used to generate audio in MacOS, Raspberry Pi, Windows and Linux. The model will have to be loaded in Max/MSP or Pure Data with the nn~ extension or SuperCollider with the nn.ar extension.</description></item></channel></rss>