Memory-Efficient Training of RNN-Transducer with Sampled Softmax

Jaesong Lee; Lukas Lee; Shinji Watanabe

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arXiv · 2203.16868

Memory-Efficient Training of RNN-Transducer with Sampled Softmax

Jaesong Lee·Lukas Lee·Shinji Watanabe

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Abstract

RNN-Transducer has been one of promising architectures for end-to-end automatic speech recognition. Although RNN-Transducer has many advantages including its strong accuracy and streaming-friendly property, its high memory consumption during training has been a critical problem for development. In this work, we propose to apply sampled softmax to RNN-Transducer, which requires only a small subset of vocabulary during training thus saves its memory consumption. We further extend sampled softmax to optimize memory consumption for a minibatch, and employ distributions of auxiliary CTC losses for sampling vocabulary to improve model accuracy. We present experimental results on LibriSpeech, AISHELL-1, and CSJ-APS, where sampled softmax greatly reduces memory consumption and still maintains the accuracy of the baseline model.

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Jaesong Lee, Lukas Lee, Shinji Watanabe. 2022-03-31. Memory-Efficient Training of RNN-Transducer with Sampled Softmax. https://arxiv.org/abs/2203.16868

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