OpenSeq2Seq: toolkit for distributed and mixed precision training of sequence-to-sequence models
OpenSeq2Seq main goal is to allow researchers to most effectively
explore various
sequence-to-sequence models. The efficiency is achieved by fully
supporting
distributed and mixed-precision training.
OpenSeq2Seq is built using TensorFlow and provides all the necessary
building blocks for training encoder-decoder models for neural machine
translation, automatic speech recognition, speech synthesis, and
language modeling.
@misc{openseq2seq,
title={Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq},
author={Oleksii Kuchaiev and Boris Ginsburg and Igor Gitman and Vitaly Lavrukhin and Jason Li and Huyen Nguyen and Carl Case and Paulius Micikevicius},
year={2018},
eprint={1805.10387},
archivePrefix={arXiv},
primaryClass={cs.CL}
}