LWMD: A Comprehensive Compression Platform for End-to-End Automatic Speech Recognition Models

Abstract

Recently end-to-end (E2E) automatic speech recognition (ASR) models have achieved promising performance. However, existing models tend to adopt increasing model sizes and suffer from expensive resource consumption for real-world applications. To compress E2E ASR models and obtain smaller model sizes, we propose a comprehensive compression platform named LWMD (light-weight model designing), which consists of two essential parts: a light-weight architecture search (LWAS) framework and a differentiable structured pruning (DSP) algorithm. On the one hand, the LWAS framework adopts the neural architecture search (NAS) technique to automatically search light-weight architectures for E2E ASR models. By integrating different architecture topologies of existing models together, LWAS designs a topology-fused search space. Furthermore, combined with the E2E ASR training criterion, LWAS develops a resource-aware search algorithm to select light-weight architectures from the search space. On the other hand, given the searched architectures, the DSP algorithm performs structured pruning to reduce parameter numbers further. With a Gumbel re-parameter trick, DSP builds a stronger correlation between the pruning criterion and the model performance than conventional pruning methods. And an attention-similarity loss function is further developed for better performance. On two mandarin datasets, Aishell-1 and HKUST, the compression results are well evaluated and analyzed to demonstrate the effectiveness of the LWMD platform.

Keywords

• automatic speech recognition
• model compression
• neural architecture search
• structured pruning