Simple ML neural network
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@Christoph-Hart Regarding the current RTNeural implementation.
With the GuitarML's AutomatedGuitarModelling LSTM trainer allows you to create parameterised models (https://github.com/GuitarML/Automated-GuitarAmpModelling).
With the NeuralNetwork module are you able to address these input parameters? I'm interested in a single input parameter and want to connect a knob to address this parameter in the model inference.
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@ccbl I believe so. When I first looked into the RTneural it appeared to be more robust than NAM. I get the appeal to be compatible with NAM captures though.
According to their github:
RTNeural is currently being used by several audio plugins and other projects:
4000DB-NeuralAmp: Neural emulation of the pre-amp section from the Akai 4000DB tape machine.
AIDA-X: An AU/CLAP/LV2/VST2/VST3 audio plugin that loads RTNeural models and cabinet IRs.
BYOD: A guitar distortion plugin containing several machine learning-based effects.
Chow Centaur: A guitar pedal emulation plugin, using a real-time recurrent neural network.
Chow Tape Model: An analog tape emulation, using a real-time dense neural network.
cppTimbreID: An audio feature extraction library.
guitarix: A guitarix effects suite, including neural network amplifier models.
GuitarML: GuitarML plugins use machine learning to model guitar amplifiers and effects.
MLTerror15: Deeply learned simulator for the Orange Tiny Terror with Recurrent Neural Networks.
NeuralNote: An audio-to-MIDI transcription plugin using Spotify's basic-pitch model.
rt-neural-lv2: A headless lv2 plugin using RTNeural to model guitar pedals and amplifiers.
Tone Empire plugins:
LVL - 01: An A.I./M.L.-based compressor effect.
TM700: A machine learning tape emulation effect.
Neural Q: An analog emulation 2-band EQ, using recurrent neural networks.
ToobAmp: Guitar effect plugins for the Raspberry Pi. -
Trying to do a simple LSTM processor project. Where should one store their json files? Or do you paste in the weights directly? Using the ScriptNode Math function.
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@Christoph-Hart ok got it. Let's say I wanted to cascade two different NNs
I'm guessing these parts will have to be modified somehow in order to differentiate between the networks. Would it just be a case of "obj1"
and "obj2"`// This contains the JSON data from `Scripts/Python/sine_model.json` const var obj = // load the sine wave approximator network nn.loadPytorchModel(obj);I'm pretty sure this is how you would set up the first part?
// We need to create & initialise the network via script, the scriptnode node will then reference // the existing network const var nn = Engine.createNeuralNetwork("NN1"); // We need to create & initialise the network via script, the scriptnode node will then reference // the existing network const var nn = Engine.createNeuralNetwork("NN2"); -
@Christoph-Hart so for now I decided to try and make a very simple audio processor using scriptnode. I followed the tutorial project adding the weights for the math.neural module to pickup, and it does see the object, however when I select it HISE crashes, complete CTD.
It's an LSTM network generated by a RTNeural based project (guitarML automatedguitarampmodelling pipline).
Not sure what the issue is.
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Attached the model in case someone wants to look at it.
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@ccbl Just did the same thing here. Used this script and it crashes HISE when I select the model:
https://github.com/GuitarML/Automated-GuitarAmpModelling
It's my first attempt, so I'm obviously doing something wrong. @Christoph-Hart is there a guide to train a model with audio files?
Also, is there a way to implement parameterized models?Dan Korneff - Producer / Mixer / Audio Nerd
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The Neural node is a promising feature.
It would be great to see an example guitar amp model (the most used case right now) with parameters.
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@orange yeah actually being able to model amps would be magnificent. I have a project in the pipeline where it would save literal gigabytes (no need to deliver processed signals).
There was also talk in another thread, I think it was the Nam (Neural AMP modeller)
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When I debug, I get failure here:
Is this caused by a key mismatch in the JSON?
@Christoph-Hart would you be able to take a quick look at this and see where it's muffed?Dan Korneff - Producer / Mixer / Audio Nerd
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@Dan-Korneff Sure I'll check if I find some time tomorrow, but I suspect there is a channel mismatch between how many channels you feed it and how many it expects.
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@Christoph-Hart That would be awesome.
The json keys look like this:{"model_data": {"model": "SimpleRNN", "input_size": 1, "skip": 1, "output_size": 1, "unit_type": "LSTM", "num_layers": 1, "hidden_size": 40, "bias_fl": true}, "state_dict": {"rec.weight_ih_l0": -
@Christoph-Hart Don't wanna load you up with too many requests, but it would be super rad if we could get this model working in scriptnode. :beaming_face_with_smiling_eyes:
Dan Korneff - Producer / Mixer / Audio Nerd
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@Dan-Korneff The model just doesn't load (and the crash is because there are no layers to process so it's a trivial out-of-bounds error.
Is it a torch or tensorflow model?
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In the homepage: https://github.com/GuitarML/Automated-GuitarAmpModelling
It says:
Using this repository requires a python environment with the 'pytorch', 'scipy', 'tensorboard' and 'numpy' packages installed.
Regarding the Neural node, parameterized FX example such as Distortion or Saturation is required which is currently only Sinus synth example is available on the snippet browser.
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@Christoph-Hart it should be a pytorch model.
This is the training script I'm testing with. It uses RTneural as a backend as well:
https://github.com/GuitarML/Automated-GuitarAmpModellingDan Korneff - Producer / Mixer / Audio Nerd
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@Dan-Korneff Ah I see, I think the Pytorch loader in HISE expects the output from this script:
https://github.com/jatinchowdhury18/RTNeural/blob/main/python/model_utils.py
which seems to have a different formatting.
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parameterized FX example such as Distortion or Saturation is required which is currently only Sinus synth example is available on the snippet browser.
The parameters need to be additional inputs to the neural network. So if you have stereo processing and 3 parameters, the network needs 5 inputs and 2 outputs. The neural network will then analyze how many channels it needs depending on the processing context and use the remaining inputs as parameters.
So far is the theory but yeah, it would be good to have a model that we can use to check if it actually works :) I'm a bit out of the loop when it comes to model creation, so let's hope we find a model that uses this structure and can be loaded into HISE.
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@Christoph-Hart said in Simple ML neural network:
I'm a bit out of the loop when it comes to model creation, so let's hope we find a model that uses this structure and can be loaded into HISE.
That'll be my homework for the day. Thanks for taking a look.
