Simple ML neural network
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@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.
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@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.
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@d-healey Ahhh got it. Thanks for the clarification.
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@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 :)develop Branch / XCode 13.1
macOS Monterey / M1 Max -
@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.
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@orange I had to take a couple days vacation over here. Back in the office Monday :)
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@Christoph-Hart Looks like Jatin has made some progress with this:
https://github.com/jatinchowdhury18/RTNeural/issues/143#issuecomment-2472915024So does this bring us closer to being able to load NAM models into RTNeural inside Hise???
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@Orvillain said in Simple ML neural network:
@Christoph-Hart Looks like Jatin has made some progress with this:
https://github.com/jatinchowdhury18/RTNeural/issues/143#issuecomment-2472915024So does this bring us closer to being able to load NAM models into RTNeural inside Hise???
Has there been any more on getting this going in HISE? Iām following this quite closely, being able to load NAM models into HISE would be a gamechanger, especially if (as someone mentioned before) it was similar to loading in convolution reverbs in terms of ease of use and control.
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@Christoph-Hart Is the CPU performance issue resolved?
We look forward to being able to use NAM models quickly and efficiently in our plugins
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Hey folks! I've been out of the loop for a while. Wondering if there has been any progress with the NAM integration?
I actually got a functional plugin working with LSTM but the training procedure is very chaotic so I haven't moved ahead much with it. I will probably provide some updates on that in a different thread though for more discussion around the particulars because I think there are other efficiencies I think might need to be ironed out.
