A Large-scale Dataset for Audio-Language Representation Learning

Luoyi Sun, Xuenan Xu, Mengyue Wu, Xie

A Large-scale Dataset for Audio-Language Representation Learning: 9 upvotes on Hugging Face Daily Papers, #6 of 9 papers on 2023-09-21. Day-by-day upvote history.

The AI community has made significant strides in developing powerful foundation models, driven by large-scale multimodal datasets. However, in the audio representation learning community, the present audio-language datasets suffer from limitations such as insufficient volume, simplistic content, and arduous collection procedures. To tackle these challenges, we present an innovative and automatic audio caption generation pipeline based on a series of public tools or APIs, and construct a large-scale, high-quality, audio-language dataset, named as Auto-ACD, comprising over 1.9M audio-text pairs. To demonstrate the effectiveness of the proposed dataset, we train popular models on our dataset and show performance improvement on various downstream tasks, namely, audio-language retrieval, audio captioning, environment classification. In addition, we establish a novel test set and provide a benchmark for audio-text tasks. The proposed dataset will be released at https://auto-acd.github.io/.

Paper page on Hugging Face · arXiv

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