Pruned RNN-T for fast, memory-efficient ASR training
Fangjun Kuang, Liyong Guo, Wei Kang, Long Lin, Mingshuang Luo, Zengwei Yao, Daniel Povey
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 memoryefficient 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.The code is open-sourced and publicly available.