nnnoiseless

Custom models

nnnoiseless is based on a neural network. There's one built in, but you can also swap out the built-in network for your own. (This might be useful, for example, if you have a particular kind of noise that you want to filter out and nnnoiseless's built-in network doesn't do a good enough job.)

Loading a nnnoiseless network

Let's suppose that you've already trained (or downloaded from somewhere) your neural network weights, and that they are in the file weights.rnn. You can use these weights for the nnnoiseless binary by passing in the --model option:

nnnoiseless --model=weights.rnn input.wav output.wav

On the other hand, if you're using nnnoiseless as a library, you can load your neural network weights using RnnModel::from_bytes or RnnModel::from_static_bytes.

Converting an RNNoise network

Some people have already made their own neural network weights for RNNoise (for example, here). These weights can be used in nnnoiseless also, but you'll need to first convert them from the (text-based) RNNoise format to the (binary) nnnoiseless format. There is a script in the train directory that can do this for you: just run

python train/convert_rnnoise.py input_file.txt output_file.rnn

Training your own weights

This is a little involved, but at least it's documented now. See train/README.md for more information.