Simple ML neural network

[aaronventure]

@scottmire keep in mind that this is GPLv3 licensed

[ccbl]

@Dan-Korneff What sample rate were you using? I'd love to train at 96khz or even 192khz for aliasing reduction reasons. My ultimate plan is to stack several smaller models of individual components together sandwiched between regular DSP so I think reducing aliasing should be important in this case.

[ccbl]

@griffinboy Here's the link to their discord channel for RTNeural

https://discord.gg/enmpURqR

[Dan Korneff]

@ccbl I'm only experimenting with 48K at the moment. Feel free to look at my repository scripts to figure out the sample rate stuff. I'm taking baby steps with this stuff while I finish up other projects.

[scottmire]

@aaronventure NAM itself is MIT licensed and this is simply a JUCE implementation of the NAM Player....so, I have no idea how they could enforce a GPLv3 license. But...I'm definitely no expert.

[d.healey]

@scottmire MIT is a weak license so you can take MIT code and relicense it pretty much however you like. If you are releasing a GPL project then all code in that project needs to be GPL. So the developer of nam-JUCE has relicensed NAM as GPL within their project.

[scottmire]

@d-healey Ahhh got it. Thanks for the clarification.

[orange]

@Dan-Korneff said in Simple ML neural network:

Using loadTensorFlowModel() was indeed the solution. I'll try to make some tutorials on training and loading models this weekend.

@Dan-Korneff @Christoph-Hart
Is there any progress? We look forward to using this neural model in a guitar amp simulation :)

[Christoph Hart]

@orange Jatin said in the GitHub issue that he's thinking about adding the wavenet model to RTNeural, when that's the case, I'll resume the work to support NAM models.

[Dan Korneff]

@orange I had to take a couple days vacation over here. Back in the office Monday :)