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.

Documentation and installation instructions

https://nvidia.github.io/OpenSeq2Seq/

Features

1. Models for:
   1. Neural Machine Translation
   2. Automatic Speech Recognition
   3. Speech Synthesis
   4. Language Modeling
   5. NLP tasks (sentiment analysis)
2. Data-parallel distributed training
   1. Multi-GPU
   2. Multi-node
3. Mixed precision training for NVIDIA Volta/Turing GPUs

Software Requirements

1. Python >= 3.5
2. TensorFlow >= 1.10
3. CUDA >= 9.0, cuDNN >= 7.0
4. Horovod >= 0.13 (using Horovod is not required, but is highly recommended for multi-GPU setup)

Acknowledgments

Speech-to-text workflow uses some parts of Mozilla DeepSpeech project.

Beam search decoder with language model re-scoring implementation (in decoders) is based on Baidu DeepSpeech.

Text-to-text workflow uses some functions from Tensor2Tensor and Neural Machine Translation (seq2seq) Tutorial.

Disclaimer

This is a research project, not an official NVIDIA product.

Related resources

• Tensor2Tensor
• Neural Machine Translation (seq2seq) Tutorial
• OpenNMT
• Neural Monkey
• Sockeye
• TF-seq2seq
• Moses

Paper

If you use OpenSeq2Seq, please cite this paper

@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}
}