VizSeq is a Python toolkit for visual analysis on text generation tasks like machine translation, summarization, image captioning, speech translation and video description. It takes multi-modal sources, text references as well as text predictions as inputs, and analyzes them visually in Jupyter Notebook or a built-in Web App (the former has Fairseq integration). VizSeq also provides a collection of multi-process scorers as a normal Python package.

[Paper] [Documentation] [Blog]

Task Coverage

Metric Coverage

Accelerated with multi-processing/multi-threading.

Getting Started

Installation

VizSeq requires Python 3.9+ and currently runs on Unix/Linux and macOS/OS X. It will support Windows as well in the future.

You can install VizSeq from PyPI repository:

$ pip install vizseq

Or install it from source:

$ git clone https://github.com/facebookresearch/vizseq
$ cd vizseq
$ pip install -e .

The base install keeps dependencies lightweight. Install optional extras only if you need them:

$ pip install vizseq[embeddings]  # LASER and BERTScore scorers (pulls in torch)
$ pip install vizseq[audio]       # reading .wav/.flac/.sph audio sources
$ pip install vizseq[translate]   # Google Translate integration
$ pip install vizseq[all]         # everything above

Documentation

Jupyter Notebook Examples

• Basic example
• Multimodal Machine Translation
• Multilingual Machine Translation
• Speech Translation

Fairseq integration

Web App Example

Download example data:

$ git clone https://github.com/facebookresearch/vizseq
$ cd vizseq
$ bash get_example_data.sh

Launch the web server:

$ vizseq-server --port 9001 --data-root ./examples/data

And then, navigate to the following URL in your web browser:

http://localhost:9001

License

VizSeq is licensed under MIT. See the LICENSE file for details.

Citation

Please cite as

@inproceedings{wang2019vizseq,
  title = {VizSeq: A Visual Analysis Toolkit for Text Generation Tasks},
  author = {Changhan Wang, Anirudh Jain, Danlu Chen, Jiatao Gu},
  booktitle = {In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  year = {2019},
}

Contact

Changhan Wang (changhan@fb.com), Jiatao Gu (jgu@fb.com)