Zero-Shot Audio Captioning Using Soft and Hard Prompts

TL;DR

This paper introduces a zero-shot audio captioning approach leveraging contrastive language-audio pre-training, enabling caption generation without audio-text paired training data and improving cross-domain robustness.

Contribution

The method uses only textual data for training and employs soft and hard prompts to enhance cross-domain generalization in zero-shot audio captioning.

Findings

Outperforms existing zero-shot methods on AudioCaps and Clotho datasets.

Demonstrates strong cross-domain generalization capabilities.

Effective in both in-domain and cross-domain scenarios.

Abstract

In traditional audio captioning methods, a model is usually trained in a fully supervised manner using a human-annotated dataset containing audio-text pairs and then evaluated on the test sets from the same dataset. Such methods have two limitations. First, these methods are often data-hungry and require time-consuming and expensive human annotations to obtain audio-text pairs. Second, these models often suffer from performance degradation in cross-domain scenarios, i.e., when the input audio comes from a different domain than the training set, which, however, has received little attention. We propose an effective audio captioning method based on the contrastive language-audio pre-training (CLAP) model to address these issues. Our proposed method requires only textual data for training, enabling the model to generate text from the textual feature in the cross-modal semantic space.In the inference stage, the model generates the descriptive text for the given audio from the audio feature by leveraging the audio-text alignment from CLAP.We devise two strategies to mitigate the discrepancy between text and audio embeddings: a mixed-augmentation-based soft prompt and a retrieval-based acoustic-aware hard prompt. These approaches are designed to enhance the generalization performance of our proposed model, facilitating the model to generate captions more robustly and accurately. Extensive experiments on AudioCaps and Clotho benchmarks show the effectiveness of our proposed method, which outperforms other zero-shot audio captioning approaches for in-domain scenarios and outperforms the compared methods for cross-domain scenarios, underscoring the generalization ability of our method.