Adaptive Compression of Supervised and Self-Supervised Models for Green Speech Recognition

Abstract

Computational power is crucial for the development and deployment of artificial intelligence capabilities, as the large size of deep learning models often requires significant resources. Compression methods aim to reduce model size making artificial intelligence more sustainable and accessible. Compression techniques are often applied uniformly across model layers, without considering their individual characteristics. In this paper, we introduce a customized approach that optimizes compression for each layer individually. Some layers undergo both pruning and/or quantization, while others are only quantized, with fuzzy logic guiding these decisions. The quantization precision is further adjusted based on the importance of each layer. Our experiments on both supervised and self-supervised models using the librispeech dataset show only a slight decrease in performance, with about 85% memory footprint reduction.