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arXiv · 2604.10438

Whisper-AuT: Domain-Adapted Audio Encoder for Efficient Audio-LLM Training

Jielin Qiu·Ming Zhu·Wenting Zhao·Zhiwei Liu·Liangwei Yang·Zixiang Chen·Roshan Ram·Akshara Prabhakar·Juntao Tan·Rithesh Murthy·Shelby Heinecke·Caiming Xiong·Silvio Savarese·Huan Wang
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Abstract

Audio-native large language models (audio-LLMs) commonly use Whisper as their audio encoder. However, Whisper was trained exclusively on speech data, producing weak representations for music and environmental sound. This forces downstream audio-LLMs to compensate through extensive training on large-scale non-speech data. We present Whisper-AuT, a domain-adapted audio encoder obtained by fine-tuning Whisper-large-v3 on a curated mixture of speech (80%), environmental sound (10%), and music (10%) totaling approximately 20M samples. The full encoder-decoder is trained end-to-end with a seq2seq captioning objective; the decoder is then discarded and only the encoder is retained. Linear probe evaluations show that Whisper-AuT achieves +23.0% on ESC-50 (environmental sound), +5.0% on GTZAN (music genre), and +0.7% on Speech Commands (keyword spotting) compared to the original Whisperlarge-v3 encoder. Whisper-AuT is designed as a drop-in replacement for Whisper in audio-LLM architectures, with the goal of reducing downstream training cost by providing stronger initial audio representations for non-speech domains.

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Jielin Qiu, Ming Zhu, Wenting Zhao, Zhiwei Liu, Liangwei Yang, Zixiang Chen, Roshan Ram, Akshara Prabhakar, Juntao Tan, Rithesh Murthy, Shelby Heinecke, Caiming Xiong, Silvio Savarese, Huan Wang. 2026-04-12. Whisper-AuT: Domain-Adapted Audio Encoder for Efficient Audio-LLM Training. https://arxiv.org/abs/2604.10438

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