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OpenSeq2Seq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence Models

2018-07-01 · WS 2018 7 · Oleksii Kuchaiev, Boris Ginsburg, Igor Gitman, Vitaly Lavrukhin, Carl Case, Paulius Micikevicius

We present OpenSeq2Seq {--} an open-source toolkit for training sequence-to-sequence models. The main goal of our toolkit is to allow researchers to most effectively explore different sequence-to-sequence architectures. The efficiency is achieved by fully supporting distributed and mixed-precision training. OpenSeq2Seq provides building blocks for training encoder-decoder models for neural machine translation and automatic speech recognition. We plan to extend it with other modalities in the future.

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Automatic Speech RecognitionAutomatic Speech Recognition (ASR)DecoderMachine Translationspeech-recognitionSpeech RecognitionTranslation

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