guitarix

Guitarix is a modular, virtual amplifier for Linux. With Guitarix you can choose different preamp and amp models and /or could load *.nam files with the Neural Amp Modeler modules, or load *.json or .aidax files with the RTNeural modules, to simulate a specific hardware unit. Combine them with various effects and speaker cabinet emulations and/or load your own Impulse Response files to come up with your very own tones. Guitarix comes as a standalone application or as vst3 plugin. Its modules are also available in the LV2 plugin format, which you can incorporate into your DAW of choice. Furthermore, it can even be run headless, so you can turn a Raspberry Pi, or any other such devices, into a dedicated amp modeler. You can even control Guitarix via a MIDI controller or foot-board.

Guitarix comes with an extensive list of effects including compression, distortion, modulation, reverb, delay, EQ, etc. Some of the effects modules that are included in Guitarix are influenced by some popular hardware units, for example the Tube Screamer is, not surprisingly, based off of the Ibanez Tube Screamer. Guitarix can also load up LADSPA and LV2 plugins to comply the effect chain.

Documentation

Standalone app

LV2 plugins

Building guitarix from source code

• git clone https://github.com/brummer10/guitarix.git
• cd guitarix
• git submodule update --init --recursive
• cd trunk
• ./waf configure --prefix=/usr --includeresampler --includeconvolver --optimization
• ./waf build
• sudo ./waf install

(build) Dependencies

• gperf,
• intltool,
• libavahi-gobject-dev,
• libbluetooth-dev
• libboost-dev,
• libboost-iostreams-dev,
• libboost-thread-dev,
• libeigen3-dev,
• libgtk-3-dev,
• libgtkmm-3.0-dev,
• libjack-dev | libjack-jackd2-dev,
• liblilv-dev,
• liblrdf0-dev,
• libsndfile1-dev,
• libfftw3-dev,
• lv2-dev,
• python3,
• sassc,

Hardware Optimization and Containerized Deployment

Guitarix can be easily built with hardware optimizations and deployed in a containerized environment using GuitarixContainer, a Docker-based solution. This approach ensures the code is automatically built for your hardware, enabling low-latency performance, especially when running Neural Amp Modeler (NAM) module. Perfect for devices like Raspberry Pi or laptop/workstation setups. Learn more at the GuitarixContainer GitHub repository.