LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation (2502.20583v1)

Abstract: Modern automatic speech recognition (ASR) models, such as OpenAI's Whisper, rely on deep encoder-decoder architectures, and their encoders are a critical bottleneck for efficient deployment due to high computational intensity. We introduce LiteASR, a low-rank compression scheme for ASR encoders that significantly reduces inference costs while maintaining transcription accuracy. Our approach leverages the strong low-rank properties observed in intermediate activations: by applying principal component analysis (PCA) with a small calibration dataset, we approximate linear transformations with a chain of low-rank matrix multiplications, and further optimize self-attention to work in the reduced dimension. Evaluation results show that our method can compress Whisper large-v3's encoder size by over 50%, matching Whisper medium's size with better transcription accuracy, thereby establishing a new Pareto-optimal frontier of efficiency and performance. The code of LiteASR is available at https://github.com/efeslab/LiteASR.

• The paper proposes LiteASR, a low-rank approximation framework using PCA on calibration activations to compress ASR encoder layers by up to 50% with minimal WER impact.
• The authors optimize self-attention by operating in reduced dimensions and implement a specialized Triton kernel for the encoder, achieving speedups of up to 1.57×.
• Extensive experiments show that LiteASR generalizes across different languages and models, demonstrating favorable trade-offs between model size and accuracy.

This paper proposes a low-rank approximation framework for compressing ASR encoder layers to achieve significant efficiency gains with minimal impact on transcription accuracy.

• The authors utilize PCA on calibration activations to decompose linear layers into low-rank matrix products, reducing encoder size by up to 50% while maintaining near-original WER performance in quality- and balanced-focused configurations.
• They further optimize self-attention by operating in reduced dimensions and implement a specialized Triton kernel derived from FlashAttention, achieving encoder speedups of up to 1.57×.
• Extensive experiments demonstrate that the approach generalizes across different languages and models, with detailed sensitivity analyses confirming favorable trade-offs between model size and accuracy.