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Pruned RNN-T for fast, memory-efficient ASR training

Authors :
Kuang, Fangjun
Guo, Liyong
Kang, Wei
Lin, Long
Luo, Mingshuang
Yao, Zengwei
Povey, Daniel
Publication Year :
2022

Abstract

The RNN-Transducer (RNN-T) framework for speech recognition has been growing in popularity, particularly for deployed real-time ASR systems, because it combines high accuracy with naturally streaming recognition. One of the drawbacks of RNN-T is that its loss function is relatively slow to compute, and can use a lot of memory. Excessive GPU memory usage can make it impractical to use RNN-T loss in cases where the vocabulary size is large: for example, for Chinese character-based ASR. We introduce a method for faster and more memory-efficient RNN-T loss computation. We first obtain pruning bounds for the RNN-T recursion using a simple joiner network that is linear in the encoder and decoder embeddings; we can evaluate this without using much memory. We then use those pruning bounds to evaluate the full, non-linear joiner network.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2206.13236
Document Type :
Working Paper