Effective and Efficient Mixed Precision Quantization of Speech Foundation Models
Haoning Xu, Zhaoqing Li, Zengrui Jin, Huimeng Wang, Youjun Chen, Guinan Li, Mengzhe Geng, Shujie Hu, Jiajun Deng, Xunying Liu·January 07, 2025
Summary
The paper presents a novel mixed-precision quantization method for speech foundation models, integrating learning and quantization in one stage. This approach enhances lossless compression ratios by up to 1.9x and 1.7x for specific models compared to uniform-precision and two-stage quantized baselines. It reduces system compression time by up to 1.9x and 1.5x while maintaining or improving word error rates. The best-performing 3.5-bit mixed-precision model achieves an 8.6x lossless compression ratio over full-precision systems.
Introduction
Background
Overview of speech foundation models
Importance of quantization in model deployment
Objective
Aim of the novel mixed-precision quantization method
Expected improvements in lossless compression ratios and system compression time
Method
Data Collection
Description of the dataset used for model training and evaluation
Data Preprocessing
Techniques applied to prepare the data for the mixed-precision quantization process
Quantization Strategy
Explanation of the integration of learning and quantization in one stage
Description of the mixed-precision approach
Model Training
Overview of the training process for the mixed-precision models
Evaluation Metrics
Criteria used to assess the performance of the models
Comparison with uniform-precision and two-stage quantized baselines
Results
Compression Ratios
Detailed comparison of lossless compression ratios for specific models
Quantification of improvements over uniform-precision and two-stage quantized baselines
Compression Time
Analysis of system compression time reductions
Comparison with uniform-precision and two-stage quantized baselines
Word Error Rates
Evaluation of the impact on word error rates
Discussion on maintaining or improving performance
Performance Analysis
Best-Performing Model
Characteristics of the 3.5-bit mixed-precision model
Detailed performance metrics and comparisons
General Observations
Summary of the method's effectiveness across different models
Insights into the trade-offs between compression and model performance
Conclusion
Summary of Contributions
Recap of the method's innovations and improvements
Future Work
Potential areas for further research and development
Implications
Discussion on the broader impact of the mixed-precision quantization method in speech foundation models
Basic info
papers
sound
audio and speech processing
artificial intelligence
Advanced features
Insights
What are the time-saving benefits of the method mentioned in the input?
How does the proposed mixed-precision quantization method improve lossless compression ratios for speech foundation models?
What is the main focus of the paper discussed in the input?
5-bit mixed-precision model in terms of lossless compression ratio compared to full-precision systems?