Soundata: A Python library for reproducible use of audio datasets

TL;DR

Soundata is a Python library that standardizes loading, validation, and reproducibility of diverse audio datasets, streamlining research workflows and reducing custom code.

Contribution

It introduces a unified, easy-to-use library for handling various audio datasets, enhancing reproducibility and standardization in audio research.

Findings

Enables quick dataset download and standardized loading

Provides tools for dataset validation against canonical versions

Improves reproducibility and reduces custom loader code

Abstract

Soundata is a Python library for loading and working with audio datasets in a standardized way, removing the need for writing custom loaders in every project, and improving reproducibility by providing tools to validate data against a canonical version. It speeds up research pipelines by allowing users to quickly download a dataset, load it into memory in a standardized and reproducible way, validate that the dataset is complete and correct, and more. Soundata is based and inspired on mirdata and design to complement mirdata by working with environmental sound, bioacoustic and speech datasets, among others. Soundata was created to be easy to use, easy to contribute to, and to increase reproducibility and standardize usage of sound datasets in a flexible way.