This is the training part of NAM. For the code to create the plugin with a trained model, see my iPlug2 fork.

You'll need at least two mono wav files: the input (DI) and the amped sound (without the cab). You can either record enough to have a training and validation set in the same file and split the file, or you can use 4 files (input/output for train/test). Also, you can provide multiple file pairs for training (or validation).

For the first option, Modify bin/train/inputs/config_data_single_pair.json to point at the audio files, and set the start/stop to the point (in samples) where the training segment ends and the validation starts. For the second option, modify and use bin/train/inputs/config_data_two_pairs.json.

Then run:

python bin/train/main.py \
bin/train/inputs/config_data.json \
bin/train/inputs/config_model.json \
bin/train/inputs/config_learning.json \
bin/train/outputs/MyAmp

Handy if you want to just check it out without going through the trouble of building the plugin.

python bin/run.py \
path/to/source.wav \
path/to/config_model.json \
path/to/checkpoints/epoch=123_val_loss=0.000010.ckpt \
path/to/output.wav

Let's get ready to rock!

python bin/export.py \
path/to/config_model.json \
path/to/checkpoints/epoch=123_val_loss=0.000010.ckpt \
path/to/exported_models/MyAmp

You'll want the HardCodedModel.h to paste over into the plugin source (i.e. here).

The model architecture in config_model.json should work plenty good. However, if you want to, you can increase the number of channels and the model will generally fit better (though it'll get closer to the threshold of real-time. 20 works for a "large" model and is still about 6x real time on my desktop).

If you want to mess with the model architecture and end up with a different receptive field (e.g. by messing with the dilation pattern), then you need to make sure that nx is changed accordingly in the data setup. The default architecture has a receptive field of 8191 samples, so nx is 8191. Generally, for the conv net architecture the receptive field is one elss than the sum of the dilations.

You can train for shorter or longer. 1000 gives pretty great results, but if you're impatient you can sometimes get away with comparable results after 500 epochs, and you might nto even be able to tell the difference with far fewer (maybe 200?...100?)