Guitar plugin made with JUCE that uses neural network models to emulate real world hardware.
See video demo on YouTube
This plugin uses a WaveNet model to recreate the sound of real world hardware. The current version models a small tube amp at clean and overdriven settings. Gain and EQ knobs were added to modulate the modeled sound.
You can create your own models and load them in SmartGuitarAmp with minor code modifications. To train your own models, use PedalNetRT
Model training is done using PyTorch on pre recorded .wav samples. More info in the above repository. To share your best models, email the json files to email@example.com and they may be included in the latest release as a downloadable zip.
Also see companion plugin, the SmartGuitarPedal
Note: As of SmartAmp version 1.3, the custom model load was removed to simplify the plugin. To load user trained models, use the SmartGuitarPedal, which plays all models trained with PedalNetRT.
Installing the plugin
- Download the appropriate plugin installer (Windows, Mac, Linux) from the Releases page.
- Run the installer and follow the instructions. May need to reboot to allow your DAW to recognize the new plugin.
Build with Cmake
# Clone the repository $ git clone https://github.com/GuitarML/SmartGuitarAmp.git $ cd SmartGuitarAmp # initialize and set up submodules $ git submodule update --init --recursive # build with CMake $ cmake -Bbuild $ cmake --build build --config Release
The binaries will be located in
This project is licensed under the Apache License, Version 2.0 - see the LICENSE file for details.
This project builds off the work done in WaveNetVA
The EQ code used in this plugin is based on the work done by Michael Gruhn in 4BandEQ algorithm.