This work presents AudioSet-Tools, a modular and extensible Python framework designed to streamline the creation of task-specific datasets derived from Google AudioSet. Despite its extensive coverage, AudioSet suffers from weak labeling, class imbalance, and a loosely structured taxonomy, which hinder its applicability in machine listening workflows. AudioSet-Tools addresses these issues through configurable taxonomy-consistent label filtering and class rebalancing strategies. The framework includes automated routines for data download and transformation, enabling reproducible and semantically consistent dataset generation for pre-training and downstream fine-tuning of deep learning models. While domain-agnostic, we showcase its versatility through AudioSet-EV, a curated subset focused on emergency vehicle siren recognition — a socially relevant and technically challenging use case that highlights structural and semantic gaps in the AudioSet taxonomy. We further provide an extensive comparative benchmark of AudioSet-EV against state-of-the-art emergency vehicle corpora. All source code and datasets are openly released on GitHub and Zenodo, fostering transparency and reproducibility in real-world audio signal processing research.
