TooB Neural Amp Modeler
A port of Steven Atkinson's Neural Amp Modeler to LV2. TooB Neural Amp Modeler uses uploadable .nam model files. Download .nam model files from http://tone3000.com, and then load them into TooB Neural Amp Modeler. The TONE3000 website contains a huge collection of community-developed amp models that can be used with TooB Neural Amp Modeler. You can also use .nam models from other sources. Using TONE3000 models with TooB Neural Amp Modeler is a two-step process. First, download model files to your local system using a web browser. Then upload the model files to the PiPedal server using the PiPedal web interface. You can find an "Upload" button in the file browser when you click on the "Model" control in the PiPedal web interface. If you are not using PiPedal, just click on the Model control, and use the file browser to select the .nam file directly on your local system. TooB Neural Amp Modeler supports a much wider range of amp models than ToobML, but usually uses more CPU. You will need at least a Pi 4 to use Toob Neural Amp Modeler, and you may need to increase your audio buffer sizes to prevent overruns. If you are having trouble with CPU usage, tone3000.com does contain some smaller amp models. Search for the "feather" tag to find models that use less CPU. Generally, you can use up to three NAM models at once in a given preset on a Pi 4, and up to 5 MAM models simultaneously on a Pi 5. N100 micro PCs could presumably do even better. However, exact CPU use varies depending on the complexity of the amp models you are using. Users have reported success using NAM on Raspberry Pi 3 devices, but this is not recommended (or really supported), as CPU usage is very high on a Raspberry Pi 3. If you are interested in profiling your own amps and effect pedals, please visit https://www.tone3000.com/capture TooB Neural Amp Modeler contains code optimizations that allow use of a third model on Raspberry PI 4 devices. These optimizations were offered to the upstream NAM project, but we recommended that they not be merged into upstream sources, because they significantly affect maintainability of the codebase. and the performance increase they provide (about 30%) is really only relevant on Raspberry PI 4-class devices. If you are interested in these optimizations, they have been published in ToobAmp project sources on GitHub. TooB Neural Amp Modeler uses code from the NeuralAmp Modeler Core project (https://www.neuralampmodeler.com/). The TooB team wishes to express gratitude to Steven Atkinson for making this revolutionary technology available as open-source code. Code from the the NeuralAmpModelerCore project (https://github.com/sdatkinson/NeuralAmpModelerCore) is provided under the following license. MIT License Copyright (c) 2025 Steven Atkinson Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Noise gate threshold. Set to minimum to disable.