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AI Research
arXiv CS.AI·Jun 11·5 min read
Towards Data-free and Training-free Compression for Speech Foundation Models Using Parameter Clustering
TL;DR
Speech foundation models often require extensive data and training for effective compression, which can be resource-intensive. This research introduces a novel technique called parameter clustering that enables data-free and training-free compression of these models.
✦ Why It Matters
Engineers can implement parameter clustering to optimize speech models without the need for extensive data or retraining.
Key Takeaways
- Explore parameter clustering for model compression.
- Achieve significant WER reductions with minimal fine-tuning.
- Implement mixed sparsity pruning for better efficiency.
- Test on LibriSpeech to validate performance improvements.
How It Works
The proposed method utilizes k-means clustering to group model parameters channelwise, allowing for a more nuanced approach to pruning. By varying the number of parameter clusters at the layer level, the model can achieve mixed sparsity, which enhances compression without the need for additional training data.