SCRAPL DDSP

Scattering with Random Paths as Loss for Differentiable Digital Signal Processing

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Instructions for Reproducibility

1. Clone this repository and open its directory.
2. Install the requirements using
   conda env create --file=conda_env_cpu.yml or
   conda env create --file=conda_env.yml
   for GPU acceleration.
   requirements_pipchill.txt and requirements_all.txt are also provided as references, but are not needed when using the conda_env.yml files.
3. The source code can be explored in the experiments/ directory.
4. All models from the paper can be found in the models/ directory.
5. Create an out directory (mkdir out).
6. Create a data directory (mkdir data).
7. All models can be evaluated by modifying scripts/validate.py and the corresponding configs/eval_ ... .yml config file and then running python scripts/validate.py.
   Make sure your PYTHONPATH has been set correctly by running a command like export PYTHONPATH=$PYTHONPATH:BASE_DIR/scrapl_ddsp/.
8. (Optional) All models can be trained by modifying scripts/train.py and the corresponding configs/train_ ... .yml config file and then running python scripts/train.py.
9. (Optional) Neutone files for running the effect models as a VST can be exported by modifying and running the scripts/export_neutone_models.py file.
10. The source code is currently not documented, but don't hesitate to open an issue if you have any questions or comments.