Pith. sign in

REVIEW

Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq

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
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original 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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.