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Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq
May 25, 2018 ยท Declared Dead ยท + Add venue
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Authors
Oleksii Kuchaiev, Boris Ginsburg, Igor Gitman, Vitaly Lavrukhin, Jason Li, Huyen Nguyen, Carl Case, Paulius Micikevicius
arXiv ID
1805.10387
Category
cs.CL: Computation & Language
Citations
52
Last Checked
4 months ago
Abstract
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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