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https://github.com/nvidia/openseq2seq
Toolkit for efficient experimentation with Speech Recognition, Text2Speech and NLP
https://github.com/nvidia/openseq2seq
deep-learning float16 language-model mixed-precision multi-gpu multi-node neural-machine-translation seq2seq sequence-to-sequence speech-recognition speech-synthesis speech-to-text tensorflow text-to-speech
Last synced: about 12 hours ago
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Toolkit for efficient experimentation with Speech Recognition, Text2Speech and NLP
- Host: GitHub
- URL: https://github.com/nvidia/openseq2seq
- Owner: NVIDIA
- License: apache-2.0
- Archived: true
- Created: 2017-09-08T20:53:07.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2021-05-11T15:50:05.000Z (over 3 years ago)
- Last Synced: 2024-11-27T03:34:51.642Z (about 2 months ago)
- Topics: deep-learning, float16, language-model, mixed-precision, multi-gpu, multi-node, neural-machine-translation, seq2seq, sequence-to-sequence, speech-recognition, speech-synthesis, speech-to-text, tensorflow, text-to-speech
- Language: Python
- Homepage: https://nvidia.github.io/OpenSeq2Seq
- Size: 57.4 MB
- Stars: 1,550
- Watchers: 93
- Forks: 369
- Open Issues: 87
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://opensource.org/licenses/Apache-2.0)
[](https://nvidia.github.io/OpenSeq2Seq/html/index.html)
# 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](https://github.com/Mozilla/DeepSpeech) project.
Beam search decoder with language model re-scoring implementation (in `decoders`) is based on [Baidu DeepSpeech](https://github.com/PaddlePaddle/DeepSpeech).
Text-to-text workflow uses some functions from [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor) and [Neural Machine Translation (seq2seq) Tutorial](https://github.com/tensorflow/nmt).
## Disclaimer
This is a research project, not an official NVIDIA product.
## Related resources
* [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor)
* [Neural Machine Translation (seq2seq) Tutorial](https://github.com/tensorflow/nmt)
* [OpenNMT](http://opennmt.net/)
* [Neural Monkey](https://github.com/ufal/neuralmonkey)
* [Sockeye](https://github.com/awslabs/sockeye)
* [TF-seq2seq](https://github.com/google/seq2seq)
* [Moses](http://www.statmt.org/moses/)
## Paper
If you use OpenSeq2Seq, please cite [this paper](https://arxiv.org/abs/1805.10387)
```
@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}
}
```