A per-layer learnable threshold, trained jointly with CTC fine-tuning, prunes 60-65% of parameters from wav2vec2-base and HuBERT-large without a statistically significant WER increase on test-clean.
In contrast, previous fine-grained ap- proach [21] performed pruning and fine-tuning separately
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Effective and Efficient One-pass Compression of Speech Foundation Models Using Sparsity-aware Self-pinching Gates
A per-layer learnable threshold, trained jointly with CTC fine-tuning, prunes 60-65% of parameters from wav2vec2-base and HuBERT-large without a statistically significant WER increase on test-clean.