Layer Pruning on Demand with Intermediate CTC

• URL: http://arxiv.org/abs/2106.09216v1
• Date: Thu, 17 Jun 2021 02:40:18 GMT
• Title: Layer Pruning on Demand with Intermediate CTC
• Authors: Jaesong Lee, Jingu Kang, Shinji Watanabe
• Abstract summary: We present a training and pruning method for ASR based on the connectionist temporal classification (CTC) We show that a Transformer-CTC model can be pruned in various depth on demand, improving real-time factor from 0.005 to 0.002 on GPU.
• Score: 50.509073206630994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying an end-to-end automatic speech recognition (ASR) model on mobile/embedded devices is a challenging task, since the device computational power and energy consumption requirements are dynamically changed in practice. To overcome the issue, we present a training and pruning method for ASR based on the connectionist temporal classification (CTC) which allows reduction of model depth at run-time without any extra fine-tuning. To achieve the goal, we adopt two regularization methods, intermediate CTC and stochastic depth, to train a model whose performance does not degrade much after pruning. We present an in-depth analysis of layer behaviors using singular vector canonical correlation analysis (SVCCA), and efficient strategies for finding layers which are safe to prune. Using the proposed method, we show that a Transformer-CTC model can be pruned in various depth on demand, improving real-time factor from 0.005 to 0.002 on GPU, while each pruned sub-model maintains the accuracy of individually trained model of the same depth.

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