Toolkit for efficient experimentation with Speech Recognition, Text2Speech and NLP
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.
https://nvidia.github.io/OpenSeq2Seq/
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.
This is a research project, not an official NVIDIA product.
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}
}