Metadata-Version: 2.1
Name: simuleval
Version: 1.1.4
Summary: SimulEval: A Flexible Toolkit for Automated Machine Translation Evaluation
Author: Xutai Ma
License: LICENSE
Keywords: SimulEval,Machine Translation,Evaluation,Metrics,BLEU,TER,METEOR,chrF,RIBES,WMD,Embedding Average,Embedding Extrema,Embedding Greedy,Embedding Average,SimulEval,SimulEval_Testing_Package_1,facebookresearch,facebook,Meta-Evaluation
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Requires-Python: >3.7.0
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pytest
Requires-Dist: pytest-cov
Requires-Dist: sacrebleu >=2.3.1
Requires-Dist: tornado
Requires-Dist: soundfile
Requires-Dist: pandas
Requires-Dist: requests
Requires-Dist: pytest-flake8
Requires-Dist: textgrid
Requires-Dist: tqdm ==4.64.1
Requires-Dist: pyyaml
Requires-Dist: bitarray ==2.6.0
Requires-Dist: yt-dlp
Requires-Dist: pydub

# SimulEval
[![](https://github.com/facebookresearch/SimulEval/workflows/build/badge.svg)](https://github.com/facebookresearch/SimulEval/actions)

SimulEval is a general evaluation framework for simultaneous translation on text and speech. Full documentation can be found [here](https://simuleval.readthedocs.io/en/v1.1.0/).

## Installation
```
git clone https://github.com/facebookresearch/SimulEval.git
cd SimulEval
pip install -e .
```

## Quick Start
Following is the evaluation of a [dummy agent](examples/quick_start) which operates wait-k (k = 3) policy and generates random words until the length of the generated words is the same as the number of all the source words.
```shell
cd examples/quick_start
simuleval --source source.txt --target target.txt --agent first_agent.py
```

# License

SimulEval is licensed under Creative Commons BY-SA 4.0.

# Citation

Please cite as:

```bibtex
@inproceedings{simuleval2020,
  title = {Simuleval: An evaluation toolkit for simultaneous translation},
  author = {Xutai Ma, Mohammad Javad Dousti, Changhan Wang, Jiatao Gu, Juan Pino},
  booktitle = {Proceedings of the EMNLP},
  year = {2020},
}
```
