This repository contains an example of how to use the RTNeural library for running inference on a neural network in realtime. The example case shown use here is in the form of an audio plugin, built using the JUCE framework.

To build from source, you must have CMake installed.

$ git clone --recursive https://github.com/jatinchowdhury18/RTNeural-example
$ cd RTNeural-example
$ cmake -Bbuild
$ cmake --build build --config Release

Builds will be output to build/RTNeuralExample_artefacts.

The audio plugin functions as a distortion effect, using a neural network to generate the distorted outut signal. The architecture and weights of the neural network are defined in neural_net.py, and then exported to neural_net_weights.json. The plugin then stores the json file as binary data, and loads the neural network at run-time.

The C++ implementation is fairly simple, but there are a few code snippets worth drawing attention to.

CMakeLists.txt:

add_subdirectory(modules/RTNeural)
include_directories(modules/RTNeural)

# Set your choice of RTNeural backends here!
set(RTNEURAL_STL ON CACHE BOOL "Use RTNeural with this backend" FORCE)
...
target_link_libraries(RTNeuralExample PUBLIC
    ...
    RTNeural
)

Note that to choose the backend used by RTNeural, you may instead set RTNEURAL_XSIMD or RTNEURAL_EIGEN.

plugin/Plugin.h

#include <RTNeural/RTNeural.h>

class RTNeuralExamplePlugin
{
...
private:
    std::unique_ptr<RTNeural::Model<float>> neuralNet[2];
};

Note that we create 2 models, since the plugin accepts 2 channels of audio (stereo).

MemoryInputStream jsonStream (BinaryData::neural_net_weights_json, BinaryData::neural_net_weights_jsonSize, false);
auto jsonInput = nlohmann::json::parse (jsonStream.readEntireStreamAsString().toStdString());
neuralNet[0] = RTNeural::json_parser::parseJson<float> (jsonInput);
neuralNet[1] = RTNeural::json_parser::parseJson<float> (jsonInput);

neuralNet[0]->reset();
neuralNet[1]->reset();

for (int ch = 0; ch < buffer.getNumChannels(); ++ch)
{
    auto* x = buffer.getWritePointer (ch);
    for (int n = 0; n < buffer.getNumSamples(); ++n)
    {
        float input[] = { x[n] };
        x[n] = neuralNet[ch]->forward (input);
    }
}

The code in this repository is licensed under the BSD 3-clause license. Enjoy!