Evaluation Code

The hear-eval-kit is used to evaluate audio embedding models on the HEAR datasets. It consists of the following functionality: 1) compute audio embeddings and 2) evaluate audio embeddings using a shallow downstream MLP classifier.

For more information on the downstream training regime used for evaluation, please see Appendix B of our paper. Information to help you get started using the hear-eval-kit is available on our github page.

[GitHub, PyPI]

Data Pre-processing Code

Individuals who would like to regenerate the HEAR benchmark tasks themselves can use hear-preprocess. However, all the HEAR benchmark tasks are available to download in a common pre-preprocessed format with human-readable metadata. The hear-preprocess package was used to process HEAR datasets into this format. It can also easily be extended to process new datasets beyond those included in the HEAR benchmark.

[GitHub, PyPI]

Baseline Models

Several baseline models that implement the common API are in included the hearbaseline Python package. Two baseline models on the leaderboard are included in this repository: wav2vec2 and CREPE.

[GitHub, PyPI]

Model Validator

hear-validator provides a command-line tool to verify that an audio embedding model conforms to the common API required by hear-eval-kit.

[GitHub, PyPI]