LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation

Keisuke Kamahori, Jungo Kasai, Noriyuki Kojima, Baris Kasikci

LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation: 13 upvotes on Hugging Face Daily Papers, #9 of 18 papers on 2025-03-03. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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