A JUCE plugin that exposes the neural DX7 model as a MIDI device, generating DX7 SysEx patches using the trained neural network.

• Neural DX7 patch generation using libtorch
• Real-time MIDI SysEx output
• Interactive latent space control
• Embedded model (no external files needed)
• Supports VST3, AU, and Standalone formats

• CMake 3.15+
• C++17 compatible compiler
• LibTorch (PyTorch C++ library)
• JUCE framework (included as submodule)

# Install system dependencies (Ubuntu/Debian)
make deps

# Setup JUCE and dependencies
make setup

# Create a dummy model file for testing
make dummy-model

# Build the project
make build

# Run standalone version
make run

1. Clone JUCE as a submodule:

   git submodule add https://github.com/juce-framework/JUCE.git JUCE
   git submodule update --init --recursive

2. Install LibTorch:

   • Download from https://pytorch.org/get-started/locally/
   • Extract and set CMAKE_PREFIX_PATH to the LibTorch directory

3. Generate the model file:

   • Follow instructions in models/README.md
   • Place dx7_vae_model.pt in the models/ directory

mkdir build
cd build
cmake .. -DCMAKE_PREFIX_PATH=/path/to/libtorch
make -j8

1. Load the plugin in your DAW or run the standalone version
2. Use the 8 sliders to control the neural model's latent space
3. Click "Generate & Send" to send DX7 patches via MIDI SysEx
4. Click "Randomize" to set random latent values
5. Connect to a DX7, Dexed, or other compatible FM synthesizer

• make all - Setup and build the project
• make setup - Initialize submodules and dependencies
• make build - Build the project
• make clean - Clean build directory
• make install - Install built plugins
• make model - Check for required model file
• make deps - Install system dependencies
• make dummy-model - Create dummy model for testing
• make run - Run standalone application
• make package - Create distribution package

• DX7VoicePacker: Handles DX7 SysEx format encoding/decoding
• NeuralModelWrapper: Manages libtorch model inference
• MidiGenerator: Handles MIDI output and device management
• PluginProcessor/Editor: JUCE plugin interface

The neural model is embedded as binary data in the executable, so no external model files are needed at runtime.

To use a real trained model instead of the dummy:

1. Train the model using the Python code in the parent directory:

   cd ../projects/dx7_vae
   python experiment.py

2. Export to TorchScript format:

   import torch
   from agoge import InferenceWorker
   
   # Load your trained model
   model = InferenceWorker('hasty-copper-dogfish', 'dx7-vae', with_data=False).model
   
   # Convert to TorchScript
   scripted_model = torch.jit.script(model)
   
   # Save the scripted model
   scripted_model.save('dx7_vae_model.pt')

3. Replace the dummy model file with the real one.