Need help with PedalNetRT?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

227 Stars 26 Forks GNU General Public License v3.0 69 Commits 8 Opened issues


Deep Learning Networks for Real Time Guitar Effect Emulation using WaveNet with PyTorch

Services available


Need anything else?

Contributors list


PedalNet-RealTime trains guitar effect/amp neural network models for use with the SmartGuitarPedal, SmartGuitarAmp, and WaveNetVA plugins. You can train a model using this repository, then convert it to a .json model that can be loaded into the VST plugin. Effective for modeling distortion style effects or tube amplifiers.

Video walkthrough of model training and usage on YouTube

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:




Email your best json models to [email protected] and they may be included in the next plugin release.


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

Notice: This project is a modified version of the original Pedalnet, from which
the model, data preparation, training, and predition scripts were obtained. 9/25/2020

For a great explanation of how it works, check out this blog post.

Setup (Locally)

Jupyter Notebooks for Google Colab are available in notebooks

  1. Install Python 3 with pip package manager
  2. Install git
  3. Create and enter virtual environment
    python -m venv .
   . bin/activate
  1. Clone the repository
    git clone src/
  2. Enter the directory and install the dependencies using
    package manager
    cd src
   python -m pip install -r requirements.txt

(Optional development dependencies)

   python -m pip install -r requirements-dev.txt

Setup (Docker)

  1. Install Docker
  2. Install (Nvidia) GPU drivers (from pytorch-lightning manual) ``` # Add the package repositories distribution=$(. /etc/os-release;echo $ID$VERSION_ID) curl -s -L | sudo apt-key add - curl -s -L$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit sudo systemctl restart docker ``

3. Create
4. Add your input and output .wav files to the
data/` directory 5. Run the docker image with desired training parameters
   docker run --rm -it \
      -v "$(pwd)"/data:data \
      -v "$(pwd)"/models:models \
      --gpus all \ data/ts9_test1_in_FP32.wav data/ts9_test1_out_FP32.wav
  1. Get your model from the


- Playing from a Fender Telecaster, bridge pickup, max tone and volume
- Split with JHS Buffer Splitter to Ibanez TS9 Tube Screamer (max drive, mid tone and volume).
- Pretrained model weights

Run effect on .wav file: Must be single channel, 44.1 kHz, FP32 wav data (not int16) ```bash python data/ts9test1inFP32.wav data/ts9test1outFP32.wav

specify input file and desired output file

python myinputguitar.wav my_output.wav

if you trained your own model you can pass --model flag

with path to .ckpt

For example:

--model=models/yourmodelname/yourmodelname.ckpt ```

Train: ```bash python data/ts9test1inFP32.wav data/ts9test1outFP32.wav

When training your own model add this flag:


python --resume # to resume training

python --gpus "0,1" # for multiple gpus python --cpu # for cpu training python -h # help (see for other hyperparameters)

The data preparation is included in, but you can run separately before training if desired:

python data/ts9test1inFP32.wav data/ts9test1outFP32.wav --model=models/yourmodelname/yourmodelname.ckpt ```


python # test pretrained model
python --model=your_trained_model.ckpt  # test trained model
Creates files
, and
, for the ground truth output, predicted output, and input signal respectively.

Model Conversion:

The .ckpt model must be converted to a .json model to run in the plugin. Usage:

python --model=your_trained_model.ckpt

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


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

Usage (after running "python --model=yourtrainedmodel.ckpt"):

python output

Training Info

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. 6. This WaveNet model is effective at reproducing distortion/overdrive, but not reverb/delay effects (or other time-based effects). Mostly untested on compressor/limiter effects, but initial results seem promising.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.