WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research (2303.17395v2)

Abstract: The advancement of audio-language (AL) multimodal learning tasks has been significant in recent years. However, researchers face challenges due to the costly and time-consuming collection process of existing audio-language datasets, which are limited in size. To address this data scarcity issue, we introduce WavCaps, the first large-scale weakly-labelled audio captioning dataset, comprising approximately 400k audio clips with paired captions. We sourced audio clips and their raw descriptions from web sources and a sound event detection dataset. However, the online-harvested raw descriptions are highly noisy and unsuitable for direct use in tasks such as automated audio captioning. To overcome this issue, we propose a three-stage processing pipeline for filtering noisy data and generating high-quality captions, where ChatGPT, a LLM, is leveraged to filter and transform raw descriptions automatically. We conduct a comprehensive analysis of the characteristics of WavCaps dataset and evaluate it on multiple downstream audio-language multimodal learning tasks. The systems trained on WavCaps outperform previous state-of-the-art (SOTA) models by a significant margin. Our aspiration is for the WavCaps dataset we have proposed to facilitate research in audio-language multimodal learning and demonstrate the potential of utilizing ChatGPT to enhance academic research. Our dataset and codes are available at https://github.com/XinhaoMei/WavCaps.

An Expert Overview of "WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research"

The paper "WavCaps" introduces a substantial contribution to the field of audio-language multimodal learning by addressing a significant gap in data availability. The authors present WavCaps, a pioneering large-scale weakly-labelled audio captioning dataset, which comprises approximately 400,000 audio clips and their associated captions. The dataset is intended to aid in overcoming the data scarcity problem prevalent in audio-language research.

Key Contributions

The paper mainly discusses the creation of WavCaps, emphasizing several innovative methodologies:

1. Data Collection and Processing: The authors sourced audio clips and their descriptions from various online platforms and an existing sound event detection dataset. Recognizing the noise present in these raw descriptions, which rendered them unsuitable for direct use, the authors devised a three-stage processing pipeline. This pipeline incorporates ChatGPT, a powerful LLM, to filter and refine these descriptions. The outcome is a dataset augmented by ChatGPT, with captions considered weakly-labelled due to the nature of this automated refinement.
2. Dataset Analysis: WavCaps is not only one of the largest audio captioning datasets but also encompasses a wider range of content than its predecessors. A comprehensive analysis highlights its diversity and scale, setting a new benchmark for the field.
3. Evaluation and Performance: The authors conducted extensive experiments across several audio-language tasks, including audio-language retrieval, automated audio captioning, zero-shot audio classification, and text-based sound generation. Models trained on WavCaps dataset consistently outperformed previous state-of-the-art models across these tasks, showcasing the utility of WavCaps in advancing audio-language multimodal research.

Implications and Future Directions

The release of WavCaps sets a new precedent in audio-language dataset curation. By leveraging ChatGPT to augment and refine raw data, the authors highlight a novel approach that could be extended to other domains where large-scale, high-quality dataset curation is challenging. This methodology paves the way for more efficient data curation processes, potentially reducing the need for costly human annotation.

Practically, WavCaps could drive improvements in deploying audio-language AI models in real-world applications, from automated captioning systems for accessibility purposes to advanced human-computer interaction devices.

Theoretically, this research introduces interesting questions regarding the balance between data scale and quality. As this dataset become a standard benchmark, researchers are encouraged to explore the implications of weakly-labelled data in training more advanced multimodal models. Moreover, the adoption and further refinement of LLMs like ChatGPT for dataset curation in other multimodal domains represent an intriguing avenue for future exploration.

In conclusion, the WavCaps dataset promises to be a cornerstone in audio-language research, significantly contributing to overcoming existing data limitations and enabling more robust model development across various audio-language tasks. The use of ChatGPT for data refinement is a particularly notable innovation, with broad implications for data-driven AI research.