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Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq

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arxiv 1805.10387 v2 pith:VPPIHJVR submitted 2018-05-25 cs.CL

Oleksii Kuchaiev , Boris Ginsburg , Igor Gitman , Vitaly Lavrukhin , Jason Li , Huyen Nguyen , Carl Case , Paulius Micikevicius This is my paper
classification cs.CL
keywords openseq2seqspeechtrainingmodelsrecognitionmachinemixed-precisiontasks
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We present OpenSeq2Seq - a TensorFlow-based toolkit for training sequence-to-sequence models that features distributed and mixed-precision training. Benchmarks on machine translation and speech recognition tasks show that models built using OpenSeq2Seq give state-of-the-art performance at 1.5-3x less training time. OpenSeq2Seq currently provides building blocks for models that solve a wide range of tasks including neural machine translation, automatic speech recognition, and speech synthesis.

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