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    Neural Amp Modeler (NAM) in HISE

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    • A
      aaronventure @Christoph Hart
      last edited by

      @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 have no idea, I'm only now touching it for the first time.

      I ran direct comparisons in Reaper and a Waveform Synth with NAM on stereo channels consumes 1% of CPU (20% of RT CPU), while two NAM plugins in total consume 0.9% of CPU and 18% of RT CPU.

      So HISE is about 10% less efficient, which is good enough and entirely shippable if you ask me.

      @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.

      OrvillainO JulesVJ 2 Replies Last reply Reply Quote 0
      • OrvillainO
        Orvillain @aaronventure
        last edited by

        @aaronventure
        1e4b768a-e69c-4c6b-a7d7-b457c9837ee1-image.png

        I'm seeing about 25% here with this. Not sure I understood about the multi container thing.

        Musician - Instrument Designer - Sonic Architect - Creative Product Owner
        Crafting sound at every level. From strings to signal paths, samples to systems.

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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.

                  A 1 Reply Last reply Reply Quote 0
                  • 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

                    JulesVJ 1 Reply Last reply Reply Quote 0
                    • 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!

                        A 1 Reply Last reply Reply Quote 1
                        • 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 😆

                          L 1 Reply Last reply Reply Quote 1
                          • L
                            Lurch @aaronventure
                            last edited by

                            @aaronventure it's been a long week 🤦 :

                            1 Reply Last reply Reply Quote 1
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