This repository handles training, reamping, and exporting the weights of a model. For playing trained models in real time in a standalone application or plugin, see the partner repo, NeuralAmpModelerPlugin.

If you don't have a good computer for training ML models, you use Google Colab to train in the cloud using the pre-made notebooks under bin\train.

For the very easiest experience, open easy_colab.ipynb on Google Colab and follow the steps!

For a little more visibility under the hood, you can use colab.ipynb instead.

Pros:

• No local installation required!
• Decent GPUs are available if you don't have one on your computer.

Cons:

• Uploading your data can take a long time.
• The session will time out after a few hours (for free accounts), so extended training runs aren't really feasible. Also, there's a usage limit so you can't hang out all day. I've tried to set you up with a good model that should train reasonably quickly!

Alternatively, you can clone this repo to your computer and use it locally.

Installation uses Anaconda for package management.

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

conda env create -f environment_gpu.yml

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

conda env create -f environment_cpu.yml

Then activate the environment you've created with

conda activate nam

After installing, you can open a GUI trainer by running

nam

from the terminal.

For users looking to get more fine-grained control over the modeling process, NAM includes a training script that can be run from the terminal. In order to run it

Download the v1_1_1.wav and overdrive.wav to a folder of your choice

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\\overdrive.wav",
        "delay": 0
    }

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

As a side 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

Exporting the trained model to a .nam file for use with the plugin can be done with:

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

Then, point the plugin at the exported model.nam file and you're good to go!

Handy if you want to just check it out without needing to use 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