We introduce AudioBench, a new benchmark designed to evaluate audio large
language models (AudioLLMs). AudioBench encompasses 8 distinct tasks and 26
carefully selected or newly curated datasets, focusing on speech understanding,
voice interpretation, and audio scene understanding. Despite the rapid
advancement of large language models, including multimodal versions, a
significant gap exists in comprehensive benchmarks for thoroughly evaluating
their capabilities. AudioBench addresses this gap by providing relevant
datasets and evaluation metrics. In our study, we evaluated the capabilities of
four models across various aspects and found that no single model excels
consistently across all tasks. We outline the research outlook for AudioLLMs
and anticipate that our open-source code, data, and leaderboard will offer a
robust testbed for future model developments.

通过提供相关数据集和评估指标，我们引入了 AudioBench，这是一个旨在评估语音大型语言模型（AudioLLMs）的新基准。我们在研究中评估了四个模型的能力，并发现没有单一模型在所有任务中都表现出色。我们概述了 AudioLLMs 的研究展望，并预计我们的开源代码、数据和排行榜将为未来模型发展提供一个强大的测试平台。