Awesome

The primary motivation of NeurST is to facilitate NLP researchers to get started on end-to-end speech translation (ST) and build advanced neural machine translation (NMT) models.

See here for a full list of NeurST examples. And we present recent progress of end-to-end ST technology at https://st-benchmark.github.io/.

NeurST is based on TensorFlow2 and we are working on the pytorch version.

<img src="http://lf3-nlp-opensource.bytetos.com/obj/nlp-opensource/neurst/icon-for-new-9.jpg" width="45">NeurST News

March 29, 2022: Release of GigaST dataset: a large-scale speech translation corpus.

Aug 16, 2021: Release of models and results for IWSLT 2021 offline ST and simultaneous translation task.

June 15, 2021: Integration of LightSeq for training speedup, see the experimental branch.

March 28, 2021: The v0.1.1 release includes the instructions of weight pruning and quantization aware training for transformer models, and several more features. See the release note for more details.

Dec. 25, 2020: The v0.1.0 release includes the overall design of the code structure and recipes for training end-to-end ST models. See the release note for more details.

Highlights

• Production ready: The model trained by NeurST can be directly exported as TF savedmodel format and use TensorFlow-serving. There is no gap between the research model and production model. Additionally, one can use LightSeq for NeurST model serving with a much lower latency.
• Light weight: NeurST is designed specifically for end-to-end ST and NMT models, with clean and simple code. It has no dependency on Kaldi, which simplifies installation and usage.
• Extensibility and scalability: NeurST has the careful design for extensibility and scalability. It allows users to customize Model, Task, Dataset etc. and combine each other.
• High computation efficiency: NeurST has high computation efficiency and can be further optimized by enabling mixed-precision and XLA. Fast distributed training using Byteps / Horovod is also supported for large-scale scenarios.
• Reliable and reproducible benchmarks: NeurST reports strong baselines with well-designed hyper-parameters on several benchmark datasets (MT&ST). It provides a series of recipes to reproduce them.

Pretrained Models & Performance Benchmarks

NeurST provides reference implementations of various models and benchmarks. Please see examples for model links and NeurST benchmark on different datasets.

• Text Translation
  • Transformer on WMT14 en->de
• Speech-to-Text Translation
  • libri-trans
  • MuST-C

Requirements and Installation

• Python version >= 3.6
• TensorFlow >= 2.3.0

Install NeurST from source:

git clone https://github.com/bytedance/neurst.git
cd neurst/
pip3 install -e .

If there exists ImportError during running, manually install the required packages at that time.

Citation

@InProceedings{zhao2021neurst,
  author       = {Chengqi Zhao and Mingxuan Wang and Qianqian Dong and Rong Ye and Lei Li},
  booktitle    = {the 59th Annual Meeting of the Association for Computational Linguistics (ACL): System Demonstrations},
  title        = {{NeurST}: Neural Speech Translation Toolkit},
  year         = {2021},
  month        = aug,
}

Contact

Any questions or suggestions, please feel free to contact us: zhaochengqi.d@bytedance.com, wangmingxuan.89@bytedance.com.

Acknowledgement

We thank Bairen Yi, Zherui Liu, Yulu Jia, Yibo Zhu, Jiaze Chen, Jiangtao Feng, Zewei Sun for their kind help.