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arXiv · 2510.12666

Structured Sparsity and Weight-adaptive Pruning for Memory and Compute efficient Whisper models

Prasenjit K Mudi·Anshi Sachan·Dahlia Devapriya·Sheetal Kalyani
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Abstract

Whisper models have achieved remarkable progress in speech recognition; yet their large size remains a bottleneck for deployment on resource-constrained edge devices. This paper proposes a framework to design fine-tuned variants of Whisper which address the above problem. Structured sparsity is enforced via the Sparse Group LASSO penalty as a loss regularizer, to reduce the number of FLOating Point operations (FLOPs). Further, a weight statistics aware pruning algorithm is proposed. We also design our custom text normalizer for WER evaluation. On Common Voice 11.0 Hindi dataset, we obtain, without degrading WER, (a) 35.4% reduction in model parameters, 14.25% lower memory consumption and 18.5% fewer FLOPs on Whisper-small, and (b) 31% reduction in model parameters, 15.29% lower memory consumption and 16.95% fewer FLOPs on Whisper-medium; and, (c) substantially outperform the state-of-the-art Iterative Magnitude Pruning based method by pruning 18.7% more parameters along with a 12.31 reduction in WER.

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Prasenjit K Mudi, Anshi Sachan, Dahlia Devapriya, Sheetal Kalyani. 2025-10-14. Structured Sparsity and Weight-adaptive Pruning for Memory and Compute efficient Whisper models. https://arxiv.org/abs/2510.12666

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