This modified version of PedalNet is meant to be used in conjuction with the SmartGuitarPedal, SmartGuitarAmp, and WaveNetVA code repositories. You can train a model using this repo, then convert it to a .json model that can be loaded into the VST plugin.

The following repositories are compatible with the converted .json model, for use with real time guitar playing through a DAW plugin or stand alone app:

https://github.com/keyth72/SmartGuitarPedal

https://github.com/keyth72/SmartGuitarAmp

https://github.com/damskaggep/WaveNetVA

Usage:

python convert_pedalnet_to_wavnetva.py --model=your_trained_model.ckpt

Generates a file named "converted_model.json" that can be loaded into the VST plugin.

You can also use "plot_wav.py" to evaluate the trained PedalNet model. By default, this will analyze the three .wav files from the test.py output. It will output analysis plots and calculate the error to signal ratio.

Usage (after running "python test.py --model=your_model.ckpt"):

python plot_wav.py

Differences from the original PedalNet (to make compatible with WaveNet plugin):

1. Uses a custom Causal Padding mode not available in PyTorch.
2. Uses a single conv1d layer for both sigm and tanh calculations, instead of two separate layers.
3. Adds a conv1d input layer.
4. Requires float32 .wav files for training (instead of int16).

Helpful tips on training models:

1. Wav files should be 3 - 4 minutes long, and contain a variety of chords, individual notes, and playing techniques to get a full spectrum of data for the model to "learn" from.
2. A buffer splitter was used with pedals to obtain a pure guitar signal and post effect signal.
3. Obtaining sample data from an amp can be done by splitting off the original signal, with the post amp signal coming from a microphone (I used a SM57). Keep in mind that this captures the dynamic response of the mic and cabinet. In the original research the sound was captured directly from within the amp circuit to have a "pure" amp signal.
4. Generally speaking, the more distorted the effect/amp, the more difficult it is to train. Experiment with different hyperparameters for each target hardware. I found that a model with only 5 channels was able to sufficiently model some effects, and this reduces the model size and allows the plugin to use less processing power.
5. When recording samples, try to maximize the volume levels without clipping. The levels you train the model at will be reproduced by the plugin. Also try to make the pre effect and post effect wav samples equal in volume levels. Even though the actual amp or effect may raise the level significantly, this isn't necessarily desirable in the end plugin.

PedalNet

Re-creation of model from Real-Time Guitar Amplifier Emulation with Deep Learning

See my blog post for a more in depth description along with song demos.

Data

data/in.wav - Concatenation of a few samples from the IDMT-SMT-Guitar dataset
data/ts9_out.wav - Recorded output of in.wav after being passed through an Ibanez TS9 Tube Screamer (all knobs at 12 o'clock).
models/pedalnet.ckpt - Pretrained model weights

Usage

Run effect on .wav file: Must be single channel, 44.1 kHz

# must be same data used to train
python prepare_data.py data/in.wav data/out_ts9.wav 

# specify input file and desired output file
python predict.py my_input_guitar.wav my_output.wav 

# if you trained you own model you can pass --model flag
# with path to .ckpt

Train:

python prepare_data.py data/in.wav data/out_ts9.wav # or use your own!
python train.py 
python train.py --gpus "0,1"  # for multiple gpus
python train.py -h  # help (see for other hyperparameters)

Test:

python test.py # test pretrained model
python test.py --model lightning_logs/version_{X}/epoch={EPOCH}.ckpt  # test trained model

Creates files y_test.wav, y_pred.wav, and x_test.wav, for the ground truth output, predicted output, and input signal respectively.