SonicCaps: Large-Scale Diverse and Fine-Grained Captioning for Improved Audio-Retrieval
Recent advances in audio-language modeling have been driven by large-scale audio captioning datasets. However, existing datasets remain limited by low semantic diversity, generic descriptions lacking acoustic details, and one-to-one audio-caption mappings that poorly reflect the inherent ambiguity of auditory perception. We introduce SonicCaps, a large-scale audio captioning dataset comprising ~15M captions paired with ~700k audio clips, generated using a multi-modal large language model (Qwen3-Omni) conditioned on both audio and text. To explicitly promote diversity, we generate around 24 captions per audio via structured prompt engineering and few- shot generation, spanning main descrip...
| Authors | Zineb Lahrichi, Marc Ferras, Gaël Richard et al. |
|---|---|
| Primary category | cs.SD |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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