# neural-amp-modeler-lv2

Bare-bones implementation of [Neural Amp Modeler](https://github.com/sdatkinson/neural-amp-modeler) (NAM) models in an LV2 plugin.

**There is no 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.

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.

For amp-only models (the most typical), **you will need to run an impulse reponse after this plugin** to model the cabinet.

### Models and Performance

The best source of models is [ToneHunt](https://tonehunt.org/).

NAM 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.

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.

If you are having trouble running a "standard" model, try looking for "feather" (the least expensive) models. You can find a list of ["feather"-tagged models on ToneHunt](https://tonehunt.org/?tags=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.

### Building

First clone the repository:
```bash
git clone --recurse-submodules -j4 https://github.com/mikeoliphant/neural-amp-modeler-lv2
cd neural-amp-modeler-lv2/build
```

Then compile the plugin using:

**Linux/MacOS**
```bash
cmake .. -DCMAKE_BUILD_TYPE="Release"
make -j4
```

**Windows**
```bash
cmake.exe -G "Visual Studio 17 2022" -A x64 ..
cmake --build . --config=release -j4
```

Note - you'll have to change the Visual Studio version if you are using a different one.

After building, the plugin will be in **build/neural_amp_modeler.lv2**.

If you have an older processor that does not support moder x64 optimizations, you may need to pass "**-DUSE_NATIVE_ARCH=OFF**" on your cmake command line.