Simple ML neural network

[ccbl]

@Dan-Korneff In this instance the dataset refers to the input output audio pairs that NAM uses for it's training, not the resulting model. Basically they're saying they added info in their training script that can detect the NAM audio pairs and train and Aida-X model based on those.

[ccbl]

@resonant that's awesome. Would love to pick their brains and see if we can get it up and running for the rest of us.

[Dan Korneff]

Here's where I'm at with the process:
https://gitlab.korneff.co/publicgroup/hise-neuralnetworktrainingscripts

I have used the scripts from https://github.com/AidaDSP/Automated-GuitarAmpModelling/tree/aidadsp_devel as a starting point.

This will allow you to create a dataset from your input/output audio file, train the model from the dataset, and then convert the model to Keras so you can use it RTNeural.

@Christoph-Hart The final model is making HISE crash. I thought I was doing something wrong because some of the values for "shape" are null, but I've downloaded other files created with the source script and they are null in the same places.

Here's one for example:
JMP Low Input.json

It's possible that the script is not formatting the json properly, but I don't know what a correct model looks like to compare to.

[Christoph Hart]

@Dan-Korneff That's the JSON from the sine generator example:

{
  "layers": "SineModel(\n  (network): Sequential(\n    (0): Linear(in_features=1, out_features=8, bias=True)\n    (1): Tanh()\n    (2): Linear(in_features=8, out_features=4, bias=True)\n    (3): Tanh()\n    (4): Linear(in_features=4, out_features=1, bias=True)\n  )\n)",
  "weights": {
    "network.0.weight": [
      [
        1.046385407447815
      ],
      [
        1.417808413505554
      ],
      [
        0.9530450105667114
      ],
      [
        1.118412375450134
      ],
      [
        -2.003693819046021
      ],
      [
        1.485351920127869
      ],
      [
        -1.323277235031128
      ],
      [
        -1.482439756393433
      ]
    ],
    "network.0.bias": [
      -0.4485535621643066,
      -1.284180760383606,
      1.995141625404358,
      -1.036547422409058,
      0.2926304638385773,
      0.4770179986953735,
      0.3244697153568268,
      0.4108103811740875
    ],
    "network.2.weight": [
      [
        -1.791297316551208,
        -0.3762974143028259,
        -0.3934035897254944,
        0.1596113294363022,
        0.5510663390159607,
        -1.115586280822754,
        0.678738534450531,
        1.327430963516235
      ],
      [
        0.3413433432579041,
        1.86607301235199,
        -0.217528447508812,
        2.568317174911499,
        0.3797312676906586,
        -0.1846907883882523,
        0.04422684758901596,
        -0.0883311927318573
      ],
      [
        0.3113365173339844,
        0.8516308069229126,
        -0.6042391061782837,
        0.9669480919837952,
        -1.354665994644165,
        0.1234097927808762,
        -1.171357274055481,
        -0.9616029858589172
      ],
      [
        -0.5073869824409485,
        -0.7385743856430054,
        0.3118444979190826,
        -0.9642266035079956,
        1.899434208869934,
        -0.1497718989849091,
        1.684132099151611,
        0.895214855670929
      ]
    ],
    "network.2.bias": [
      -0.6971003413200378,
      0.3228396475315094,
      -0.6209602355957031,
      0.1816271394491196
    ],
    "network.4.weight": [
      [
        -0.9233435988426208,
        1.108147859573364,
        -0.8966623544692993,
        0.394584596157074
      ]
    ],
    "network.4.bias": [
      0.06727132201194763
    ]
  }
}

So apparently it doesn't resolve the python code for defining the layer composition but uses a single string that is parsed. That's the output of a custom python script I wrote and run on a model built with TorchStudio, but if your model is "the standard" way, I'll make sure that it loads correctly too as these things look like syntactic sugar to me.

[Christoph Hart]

Here's the link to the tutorial again:

hise_tutorial/NeuralNetworkExample/Scripts/python at master · christophhart/hise_tutorial

The Tutorial project for HISE. Contribute to christophhart/hise_tutorial development by creating an account on GitHub.

GitHub (github.com)

But I realized your example looks more or less like the Tensorflow model in this directory. Which method are you using for loading the model?

[Lindon]

@Dan-Korneff said in Simple ML neural network:

@resonant Invite them to the conversation

@resonant
yeah, maybe - they asked me to get re-involved with them on some ML stuff as they were a bit stuck.....it didnt go anywhere, so they may well still be stuck or they found someone else to do the coding for them....your call

[Dan Korneff]

@Christoph-Hart The structure does look like TensorFlow, but the script is using torch libraries to create the model:

torch
torch.optim
torch.cuda
torch.optim.Adam
torch.optim

I was using this example code to load the model:

const var pt = Engine.createNeuralNetwork("PytorchNetwork");;

// Load the model layout & weights that were exported as JSON
const var modelJSON = pythonRoot.getChildFile("model_keras.json").loadAsObject();

// Load the model & weights:
pt.loadPytorchModel(modelJSON);

when I load the model into the Neural node, HISE checks out.

I've run through the MNIST dataset tutorial a couple times to get a basic idea of how TorchStudio works, but I'm not sure how to adapt the scripts to work there.

[ccbl]

I asked Jatin to have a quick look through the thread to see if he could see any issues, he just had this to say.

"Hmmm, it seems to me that the model JSON file that is being loaded into the "Neural Node" is structured as a TensorFlow-style JSON file, but it's being loaded with the HISE's loadPytorchModel() method? I don't really know what the Neural Node does internally, so idk how much I can help beyond that."

[Dan Korneff]

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

[Lindon]

@Dan-Korneff that would be very cool. Do these models include the ability to define parameters(like tone controls on an amp)? Or are they static snap shots?

[Dan Korneff]

@Lindon said in Simple ML neural network:

@Dan-Korneff that would be very cool. Do these models include the ability to define parameters(like tone controls on an amp)? Or are they static snap shots?

The training scripts can create parameterized models, but I've only tested a static model so far.
Once I get my sea legs I'll pull in @Christoph-Hart to figure out multiple parameters.

[aaronventure]

@Dan-Korneff man this sounds great, thanks.

how are you finding the results so far comparing the capture and the model output?

[Dan Korneff]

@aaronventure I haven't gotten that far yet. Still in the "does this even work" stage

[griffinboy]

@ccbl

Chowdhury? I'd love to talk to him! His dsp is very inspiring

[scottmire]

Apologies if this has already been discussed....but I would think this would be low hanging fruit:

GitHub - Tr3m/nam-juce: A JUCE implementation of the Neural Amp Modeler Plugin

A JUCE implementation of the Neural Amp Modeler Plugin - Tr3m/nam-juce

GitHub (github.com)

[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.