Papers › Zero-Shot Audio Captioning via Audibility Guidance

Zero-Shot Audio Captioning via Audibility Guidance

7 Sep 2023arXiv:2309.03884archive 2025-07-28

Tal Shaharabany, Ariel Shaulov, Lior Wolf

The task of audio captioning is similar in essence to tasks such as image and video captioning. However, it has received much less attention. We propose three desiderata for captioning audio -- (i) fluency of the generated text, (ii) faithfulness of the generated text to the input audio, and the somewhat related (iii) audibility, which is the quality of being able to be perceived based only on audio. Our method is a zero-shot method, i.e., we do not learn to perform captioning. Instead, captioning occurs as an inference process that involves three networks that correspond to the three desired qualities: (i) A Large Language Model, in our case, for reasons of convenience, GPT-2, (ii) A model that provides a matching score between an audio file and a text, for which we use a multimodal matching network called ImageBind, and (iii) A text classifier, trained using a dataset we collected automatically by instructing GPT-4 with prompts designed to direct the generation of both audible and inaudible sentences. We present our results on the AudioCap dataset, demonstrating that audibility guidance significantly enhances performance compared to the baseline, which lacks this objective.

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Tasks

Language ModelingZero-shot Audio Captioning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Zero-shot Audio Captioning AudioCaps Shaharabany et al. BLEU-4 9.8 #3 of 4 Archive leaderboard report
Zero-shot Audio Captioning AudioCaps Shaharabany et al. CIDEr 9.2 #3 of 4 Archive leaderboard report
Zero-shot Audio Captioning AudioCaps Shaharabany et al. METEOR 8.6 #3 of 4 Archive leaderboard report
Zero-shot Audio Captioning AudioCaps Shaharabany et al. ROUGE-L 8.2 #3 of 4 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionAttention DropoutBPECosine AnnealingDense ConnectionsDiscriminative Fine-TuningDropoutGPT-2GPT-4Label SmoothingLayer NormalizationLinear LayerLinear Warmup With Cosine AnnealingMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformerWeight Decay

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