  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.
