AIDA-X vs Neural Amp Modeler

AIDA-X is a free AI amp model player for loading and playing music-gear models, primarily for high-fidelity amplifier simulations. Signal chains can combine amp and cabinet models with distortion, drive, fuzz, boost, and EQ. The desktop plugin supports CLAP, LV2, VST2, and VST3, and a standalone version is available. Its cabinet simulator follows the amp model and accepts WAV or FLAC impulse-response files. Controls include input and output meters, bypass, input level, EQ tone controls, and output level. Desktop installers are provided for macOS and Windows, with Linux binary tarballs. The experimental browser version runs in WebAssembly-capable browsers and lets users load their own amp models and cabinet IRs, but it uses preselected guitar loops instead of audio input. The standalone app connects to the system-default audio device and does not offer another input or output selection. Desktop builds are unsigned and may trigger untrusted-developer warnings. AIDA-X is licensed under GPL-3.0-or-later.

Neural Amp Modeler (NAM) uses deep learning to create models of guitar amplifiers and pedals. The project is free and open source, with three code repositories under permissive licenses that are open to contributions. Users can train models online in Google Colab or install the Python-based trainer locally from PyPI. The Gateway plugin loads and plays snapshot models of gear, while ParametricOD demonstrates modeling across an overdrive's knobs and switches. The plugin repository produces VST3 and AU plugins as well as a standalone desktop application. Models can be shared through TONE3000, a community-organized online library. NAM runs on Windows, macOS, Linux, Raspberry Pi, embedded systems, and websites. Existing A1 models remain supported in NeuralAmpModelerCore and NeuralAmpModelerPlugin. Builders can incorporate NAM's open-source modeling technology into their own products. The training code depends on PyTorch Lightning; version 0.12.3 excludes compromised Lightning versions 2.6.2 and 2.6.3.