AU-Harness: An Open-Source Toolkit for Holistic Evaluation of Audio LLMs
Authors: name: Sidharth Surapaneni, name: Hoang Nguyen, name: Jash Mehta, name: Aman Tiwari, name: Oluwanifemi Bamgbose, name: Akshay Kalkunte, name: Sai Rajeswar, name: Sathwik Tejaswi Madhusudhan

Large Audio Language Models (LALMs) are rapidly advancing, but evaluating
them remains challenging due to inefficient toolkits that limit fair comparison
and systematic assessment. Current frameworks suffer from three critical
issues: slow processing that bottlenecks large-scale studies, inconsistent
prompting that hurts reproducibility, and narrow task coverage that misses
important audio reasoning capabilities. We introduce AU-Harness, an efficient
and comprehensive evaluation framework for LALMs. Our system achieves a speedup
of up to 127% over existing toolkits through optimized batch processing and
parallel execution, enabling large-scale evaluations previously impractical. We
provide standardized prompting protocols and flexible configurations for fair
model comparison across diverse scenarios. Additionally, we introduce two new
evaluation categories: LLM-Adaptive Diarization for temporal audio
understanding and Spoken Language Reasoning for complex audio-based cognitive
tasks. Through evaluation across 380+ tasks, we reveal significant gaps in
current LALMs, particularly in temporal understanding and complex spoken
language reasoning tasks. Our findings also highlight a lack of standardization
in instruction modality existent across audio benchmarks, which can lead up
performance differences up to 9.5 absolute points on the challenging complex
instruction following downstream tasks. AU-Harness provides both practical
evaluation tools and insights into model limitations, advancing systematic LALM
development.