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 When the Threaded control is on, neural amp modelling calculations are run on a separate thread from the real-time audio thread. This introduces one extra audio buffer of latency, but allows the plugin to make full use of all availble processor cores when running more than one instance of TooB Neural Amp Modeler. As a result, you can run more instances of TooB Neural Amp Modeler than you ordinarily could. On a Pi 5, you can run up to 8 instances of TooB Neural Amp Modeler in a single preset (as opposed to four or five), and up to 5 instances of TooB Neural Amp Modeler (as opposed to two or three). You need to check that the PiPedal server's CPU is not temperature throttling when using this feature. If the CPU temperature exceeds 80 degrees Celcius on a Raspberry Pi, the CPU clock rate is reduced, and the performance of the plugins will be affected. Having a good heat sink, or active cooling system is recommended when using this feature. PiPedal displays the current CPU temperature in a status message at the bottom of the display (unless you've turned that off in settings), and also displays the current CPU temperature in the status message found at the top of the Settings page. The exact temperature at which the CPU starts to throttle varies from computer to computer. If you are running on a system that is not a Raspberry Pi, please consult your manufacturer's help documentation to find out what temperature heat throttling starts at on your particular computer, if you really need to know what the threshold is; but the effect on performance is pretty obvious when heat throttling does start. Note that heat throttling is done to protect your processor from permanent damage due to overheating; but there's no real risk of actual damage, since heat throttling thresholds are set well below the point at which damage occurs. 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. This is groundbreaking technology that changes everything; and work that would be impossible to replicate without the work that Steven has done. TooB Neural Amp Modeler uses code from the NeuralAudio project (https://www.neuralampmodeler.com/). The Neural Audio project provides highly optimized implementations of the most commonly used Neural Amp models. The TooB team wishes to express gratitude to Mike Oliphant for making his making his optimizations available as open-source code. Steve and Mike have done all of the hard work that was required to make NAM technology available through TooB Neural Amp Modeler. 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. Code from the the NeuralAudio project (https://github.com/sdatkinson/NeuralAmpModelerCore) is provided under the following license. MIT License Copyright (c) 2024 Mike Oliphant 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.
Run computation on a separate thread. This allows more instances of NAM to run simultaneously, but adds one extra audio buffer of latency.
Noise gate threshold. Set to minimum to disable.