The Kernel Audio Distance Toolkit (KADTK) provides an efficient and standardized implementation of Kernel Audio Distance (KAD)—a distribution-free, unbiased, and computationally efficient metric for evaluating generative audio.

To use the KAD toolkit, you must first install it. This library is created and tested on Python 3.10 on Linux but should work on Python >=3.9,<3.12.

Requirement: Install torch here (for previous versions); only torch >=2.1,<2.6 officially supported.
Requirement: Install tensorflow (>=2.0) here; choose the appropriate version for CUDA compatibility here - note that pip usually does not perform this check.

To install kad toolkit, run:

pip install kadtk

(to reproduce our exact tested environment,

git clone https://github.com/YoonjinXD/kadtk.git && 
cd kadtk && 
pip install poetry==2.0.1 && 
poetry install && 
pip install -e .

)

• if scipy causes some error, reinstall scipy: pip uninstall scipy && pip install scipy==1.11.2
• if charset causes some error, (re)install chardet: pip install chardet
• if CUDA causes some error, ensure your device is GPU-compatible and install the necessary software for CUDA support.

The toolkit provides a CLI command for computing KAD scores. It automatically extracts embeddings and computes the KAD score between your reference set (e.g. ground truth) and target evaluation set (e.g. generated audio).

kadtk {model_name} {reference-set dir} {target-set dir}

Note that:

• KAD generally has a different value when the reference set and the target set are switched, because the kernel bandwidth for the MMD is calculated as the median distance between the embeddings of the reference set. This is to ensure that the score takes on a consistent meaning even when the target set is changed.
• KAD is based on an unbiased, finite-sample estimation of the MMD; it may take on negative values if there are too few samples and/or if the two embedding sets are very close in distribution. Refer to our paper for more details. Make sure that the reference set always contains the ground truth samples (e.g. Audiocaps or Clotho for text-to-audio), and that the target set contains the generated samples.

(Enable Options)

--fad compute Fréchet Audio Distance instead of Kernel Audio Distance.
--inf option uses metric-inf extrapolation, and --indiv calculates metric for individual audios.
--force-emb-encode forces re-extraction of embeddings, not using cache.
--force_stats-calc forces re-calculation of kernel statistics, not using cache.

(Examples)

kadtk panns-wavegram-logmel {reference-set dir} {target-set dir} # will calulcate kad btw 2 dirs(each dirs should contains wav files)
kadtk vggish {reference-set dir} {target-set dir} --fad # will calculate FAD instead of KAD
kadtk passt-fsd50k {reference-set dir} {target-set dir} --csv scores.csv # will save results in scores.csv
kadtk-embeds -m wavlm-base -d {reference-set dir} {target-set dir} # will only save each embeddings

Optionally, you can install dependencies that add additional embedding support. They are:

• CDPAM: pip install cdpam
• DAC: pip install descript-audio-codec==1.0.0

@article{kad,
    author={Chung, Yoonjin and Eu, Pilsun and Lee, Junwon and Choi, Keunwoo and Nam, Juhan and Chon, Ben Sangbae},
    title={KAD: No More FAD! An Effective and Efficient Evaluation Metric for Audio Generation}, 
    journal = {arXiv:2502.15602},
    url = {https://arxiv.org/abs/2502.15602},
    year = {2025}
}

We sincerely thank the authors of the following papers for sharing the code as open source: fadtk fadtk with panns

@article{fad_embeddings,
    author = {Tailleur, Modan and Lee, Junwon and Lagrange, Mathieu and Choi, Keunwoo and Heller, Laurie M. and Imoto, Keisuke and Okamoto, Yuki},
    title = {Correlation of Fréchet Audio Distance With Human Perception of Environmental Audio Is Embedding Dependant},
    journal = {arXiv:2403.17508},
    url = {https://arxiv.org/abs/2403.17508},
    year = {2024}
}

@inproceedings{fadtk,
  title = {Adapting Frechet Audio Distance for Generative Music Evaluation},
  author = {Azalea Gui, Hannes Gamper, Sebastian Braun, Dimitra Emmanouilidou},
  booktitle = {Proc. IEEE ICASSP 2024},
  year = {2024},
  url = {https://arxiv.org/abs/2311.01616},
}