aiqbench analysis
This research introduces a parameter clustering technique for compressing speech foundation models like HuBERT without requiring additional training or access to original datasets, addressing critical efficiency constraints in deploying large AI systems. By achieving significant word error rate reductions over magnitude-based pruning while maintaining baseline performance after minimal fine-tuning, the method offers a viable path toward lightweight yet accurate audio processing architectures.
Towards Data-free and Training-free Compression for Speech Foundation Models Using Parameter Clustering
arXiv:2606.11836v2 Announce Type: replace-cross Abstract: This paper presents a novel data-free and training-free compression approach for speech foundation models using channelwise clustering via k-means. More fine-grained, mixed sparsity pruning by layer-level varying number of parameter clusters is also explored. Experiments conducted on the LibriSpeech dataset suggest that when operating with pruning sparsity of 50% on HuBERT-large, consistent WER reductions of 27.73%/18.61% absolute (34.37%/21.91% relative) over the magnitude-based pruning were obtained on the test-clean and test-other subsets before fine-tuning and 0.19%/0.79% absolute (3.36%/4.62% relative) after fine-tuning with only 3 epochs. Similar WER reductions of 2.86%/5.02% absolute (59.21%/55.29% relative) were observed against magnitudebased pruning on Whisper-large-v3 at 10% sparsity, all with no significant WER increase relative to the uncompressed baseline.
Abstract:This paper presents a novel data-free and training-free compression approach for speech foundation models using channelwise clustering via k-means. More fine-grained, mixed sparsity pruning by layer-level varying number of parameter clusters is also explored. Experiments conducted on the LibriSpeech dataset suggest that when operating with pruning sparsity of 50% on HuBERT-large, consistent WER reductions of 27.73%/18.61% absolute (34.37%/21.91% relative) over the magnitude-based pruning were obtained on the test-clean and test-other subsets before fine-tuning and 0.19%/0.79% absolute (3.36%/4.62% relative) after fine-tuning with only 3 epochs. Similar WER reductions of 2.86%/5.02% absolute (59.21%/55.29% relative) were observed against magnitudebased pruning on Whisper-large-v3 at 10% sparsity, all with no significant WER increase relative to the uncompressed baseline.
Sound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
arXiv:2606.11836 [cs.SD]
(or arXiv:2606.11836v2 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2606.11836 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 212 words