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    Neural Amp Modeler (NAM) in HISE

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    • A
      aaronventure @Orvillain
      last edited by

      @Orvillain you can still see the two cables going into it. You multi node should contain a neural node and an empty chain node.

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      • whoopsydoodleW
        whoopsydoodle
        last edited by

        How well does this nam implementation handle different sample rates? I know some get tripped up if you try to run a sample rate that's different than what your model was trained on.

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        • S
          scottmire
          last edited by

          Strange....
          I'm getting a "function not found" error when calling loadNAMModel

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          • JulesVJ
            JulesV @Christoph Hart
            last edited by JulesV

            @Christoph-Hart said in Neural Amp Modeler (NAM) in HISE:

            Haha wasn't all that drama about it being 8x slower than the NAM plugin?

            I think it was the AidaX, not NAM: https://forum.hise.audio/topic/11326/8-times-more-cpu-consumption-on-aida-x-neural-models

            @aaronventure So, apart from AidaX, NAM performance is close to its own plugin, right? Great news then.

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            • JulesVJ
              JulesV @aaronventure
              last edited by JulesV

              @aaronventure said in Neural Amp Modeler (NAM) in HISE:

              @Christoph-Hart said in Neural Amp Modeler (NAM) in HISE:

              Can't you just embed the JSON content of the NAM file into a script and it will be embedded in the plugin?

              I sure can. I feel silly now. Thank you.

              Have you ever tried embedding NAM content as JSON like @Christoph-Hart adviced?

              As you mentioned above, I can open them externally, but embed doesn't work here.

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              • A
                aaronventure @JulesV
                last edited by

                @JulesV i haven't gotten around to actually implementing this, but it should work, instead of just reading the file as object, i would just paste the data in there

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                • JulesVJ
                  JulesV @aaronventure
                  last edited by JulesV

                  @aaronventure said in Neural Amp Modeler (NAM) in HISE:

                  @JulesV i haven't gotten around to actually implementing this, but it should work, instead of just reading the file as object, i would just paste the data in there

                  This is the nam file (to upload here, I changed the extension to .txt, but you need to change it to .nam after downloading): A73.txt

                  Using this externally works. But for JSON, Hise cannot load this complex data, which has a lot of weights, and it hangs then crashes.

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                  • L
                    Lurch @JulesV
                    last edited by Lurch

                    @JulesV Don't know if this helps but I have this working fine in my current project:

                    //ML Model Loader
                    const neuralNetwork = Engine.createNeuralNetwork("Plasma");
                    
                    const namModel = FileSystem.getFolder(FileSystem.AudioFiles).getChildFile("Plasma.json").loadAsObject();
                    
                    neuralNetwork.loadNAMModel(namModel);
                    ;
                    
                    

                    I renamed my NAM model to a JSON, in this case Plasma.json and put it in AudioFiles as I found Samples wasn't working reliably for me.
                    This then pops up in the Neural node in scriptnode no problem. This is a 1000 Epoch NAM model so the json is pretty big/has a lots of weights.
                    Again, hope this helps!
                    And thanks so much @aaronventure for the help getting this going, it's been a bit of a game changer for me distortion wise!

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                    • A
                      aaronventure @Lurch
                      last edited by

                      @Lurch said in Neural Amp Modeler (NAM) in HISE:

                      EDIT: Ignore the Const Var at the end there, copied by accident, doing a few things at once.

                      this is funny because you could've used the edit to remove it 😆

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                      • L
                        Lurch @aaronventure
                        last edited by

                        @aaronventure it's been a long week 🤦 :

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