StableQuant: Layer Adaptive Post-Training Quantization for Speech Foundation Models

StableQuant: Layer Adaptive Post-Training Quantization for Speech Foundation Models Y eona Hong, Hyewon Han, Woo-jin Chung, and Hong-Goo Kang Department of Electrical and Electronic Engineering, Y onsei University Seoul, Republic of Korea {yeonahong, hwhan, woojinchung }@dsp.yonsei.ac.kr, hgkang@yonsei.ac.kr Abstract --In this paper, we propose StableQuant, a novel adaptive post-training quantization (PTQ) algorithm for widely used speech foundation models (SFMs). While PTQ has been successfully employed for compressing large language models (LLMs) due to its ability to bypass additional fine-tuning, directly applying these techniques to SFMs may not yield optimal results, as SFMs utilize distinct network architecture for feature extraction. StableQuant demonstrates optimal quantization performance regardless of the network architecture type, as it adaptively determines the quantization range for each layer by analyzing both the scale distributions and overall performance. We evaluate our algorithm on two SFMs, HuBERT and wav2vec2.0, StableQuant successfully reduces the sizes of SFM models to a quarter and doubles the inference speed while limiting the word error rate (WER) performance drop to less than 0. 3% with 8-bit quantization.