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Mike Oliphant
53 lines
2.7 KiB
Markdown
53 lines
2.7 KiB
Markdown
# neural-amp-modeler-lv2
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LV2 plugin for using neural network machine learning amp models.
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**There is no custom plugin user interface**. Setting the model to use requires that your LV2 host supports atom:Path parameters. Reaper does as of v6.82. Carla and Ardour do. If your favorite LV2 host does not support atom:Path, let them know you want it.
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If you are looking for a GUI version, @brummer10 [has one here](https://github.com/brummer10/neural-amp-modeler-ui) that works for Linux and Windows. You may also be interested in the the version shipped with the [MOD Desktop App](https://github.com/moddevices/mod-desktop-app), or my digital pedalboard app [Stompbox](https://github.com/mikeoliphant/StompboxUI).
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To get the intended behavior, **you must run your audio host at the same sample rate the model was trained at** (usually 48kHz) - no resampling is done by the plugin.
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For amp-only models (the most typical), **you will need to run an impulse reponse after this plugin** to model the cabinet.
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### Models and Performance
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The plugin supports both [Neural Amp Modeler (NAM)](https://github.com/sdatkinson/neural-amp-modeler) models and [RTNeural keras json models](https://github.com/jatinchowdhury18/RTNeural) (like those used by [Aida-X](https://github.com/AidaDSP/AIDA-X)).
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The best source of models is [ToneHunt](https://tonehunt.org/).
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NAM WaveNet models are generally quite expensive to run. This isn't (much of) an issue on modern PCs, but you may have trouble running on less powerful hardware.
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A Raspberry Pi 4 running a 64bit OS can run "standard" NAM models with a bit of room to spare for a cabinet IR and some lightweight effects.
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If you are having trouble running a "standard" model, try looking for "feather", or even "nano" (the least expensive) models. You can find a list of ["feather"-tagged models on ToneHunt](https://tonehunt.org/models?tags%5B0%5D=feather-mdl). Note that tagging models is up to the submitter, so not all "feather" models are tagged as such - you should be able to find more if you dig around.
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### Building
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First clone the repository:
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```bash
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git clone --recurse-submodules -j4 https://github.com/mikeoliphant/neural-amp-modeler-lv2
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cd neural-amp-modeler-lv2/build
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```
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Then compile the plugin using:
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**Linux/MacOS**
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```bash
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cmake .. -DCMAKE_BUILD_TYPE="Release"
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make -j4
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```
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**Windows**
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```bash
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cmake.exe -G "Visual Studio 17 2022" -A x64 ..
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cmake --build . --config=release -j4
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```
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Note - you'll have to change the Visual Studio version if you are using a different one.
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After building, the plugin will be in **build/neural_amp_modeler.lv2**.
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### Optimization
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If you have a relatively modern x64 processor, you can pass "**‑DUSE_NATIVE_ARCH=ON**" on your cmake command line to enable certain processor-specific optimizations.
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