Training locally from the command line

The command line trainer is the full-featured option for training models with NAM.

Installation

Currently, you’ll want to clone the source repo to train from the command line.

Installation uses Anaconda for package management.

For computers with a CUDA-capable GPU (recommended):

conda env create -f environment_gpu.yml

Note

You may need to modify the CUDA version if your GPU is older. Have a look at nVIDIA’s documentation if you’re not sure.

Otherwise, for a CPU-only install (will train much more slowly):

conda env create -f environment_cpu.yml

Note

If Anaconda takes a long time “Solving environment…”, then you can speed up installing the environment by using the mamba experimental sovler with --experimental-solver=libmamba.

Then activate the environment you’ve created with

conda activate nam

Training

Since the command-line trainer is intended for maximum flexibiility, you can train from any input/output pair of reamp files you want. However, if you want to skip the reamping and use some pre-made files for your first time, you can download these files:

Next, edit bin/train/data/single_pair.json to point to relevant audio files:

"common": {
    "x_path": "C:\\path\\to\\v1_1_1.wav",
    "y_path": "C:\\path\\to\\output.wav",
    "delay": 0
}

Note

If you’re providing your own audio files, then you need to provide the latency (in samples) between the input and output file. A positive number of samples means that the output lags the input by the provided number of samples; a negative value means that the output precedes the input (e.g. because your DAW over-compensated). If you’re not sure exactly how much latency there is, it’s usually a good idea to add a few samples just so that the model doesn’t need to predict the future!

Next, to train, open up a terminal. Activate your nam environment and call the training with

python bin/train/main.py \
bin/train/inputs/data/single_pair.json \
bin/train/inputs/models/demonet.json \
bin/train/inputs/learning/demo.json \
bin/train/outputs/MyAmp

  • data/single_pair.json contains the information about the data you’re training on.

  • models/demonet.json contains information about the model architecture that is being trained. The example used here uses a feather configured wavenet.

  • learning/demo.json contains information about the training run itself (e.g. number of epochs).

The configuration above runs a short (demo) training. For a real training you may prefer to run something like:

python bin/train/main.py \
bin/train/inputs/data/single_pair.json \
bin/train/inputs/models/wavenet.json \
bin/train/inputs/learning/default.json \
bin/train/outputs/MyAmp

Note

NAM uses PyTorch Lightning under the hood as a modeling framework, and you can control many of the PyTorch Lightning configuration options from bin/train/inputs/learning/default.json.

Once training is done, a file called model.nam is created in the output directory. To use it, point the plugin at the file and you’re good to go!