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Zero-shot audio captioning aims at automatically generating descriptivetextual captions for audio content without prior training for this task.Different from speech recognition which translates audio content that containsspoken language into text, audio captioning is commonly concerned with ambientsounds, or sounds produced by a human performing an action. Inspired byzero-shot image captioning methods, we propose ZerAuCap, a novel framework forsummarising such general audio signals in a text caption without requiringtask-specific training. In particular, our framework exploits a pre-trainedlarge language model (LLM) for generating the text which is guided by apre-trained audio-language model to produce captions that describe the audiocontent. Additionally, we use audio context keywords that prompt the languagemodel to generate text that is broadly relevant to sounds. Our proposedframework achieves state-of-the-art results in zero-shot audio captioning onthe AudioCaps and Clotho datasets. Our code is available athttps://github.com/ExplainableML/ZerAuCap.

Zero-shot audio captioning with audio-language model guidance and audio context keywords