README.md

metadata

license: mit
task_categories:
  - translation
  - audio-to-audio
language:
  - de
  - en
  - es
  - fr
  - it
  - zh
size_categories:
  - 1M<n<10M

Dataset Card for Seamless-Align-Expressive (WIP). Inspired by https://huggingface.co/datasets/allenai/nllb

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: [Needs More Information]
• Repository: [Needs More Information]
• Paper: [Needs More Information]
• Leaderboard: [Needs More Information]
• Point of Contact: [Needs More Information]

Dataset Summary

This dataset was created based on metadata for mined expressive Speech-to-Speech(S2S) released by Meta AI. The S2S contains data for 5 language pairs. The S2S dataset is ~228GB compressed.

How to use the data

There are two ways to access the data:

• Via the Hugging Face Python datasets library

Scripts coming soon

• Clone the git repo

git lfs install
git clone https://huggingface.co/datasets/jhu-clsp/seamless-align-expressive

Supported Tasks and Leaderboards

N/A

Languages

Language pairs can be found here.

Dataset Structure

Each language pair contains two gzipped files, src.tar.gz and tgt.tar.gz

Data Instances

Language Pair	Number of samples
de-en	1385380
en-es
en-fr
en-it
en-zh

Data Fields

Data Field can be found here.

Data Splits

The data is not split.

Dataset Creation

Curation Rationale

Source Data

Inspect links in metadata

Who are the source language producers?

Speech was collected from the web many of which are web crawls.

Annotations

Annotation process

Parallel sentences were identified using SONAR Expressive encoders. (Duquenne et al., 2023)

Who are the annotators?

The data was not human annotated.

Personal and Sensitive Information

Data may contain personally identifiable information, sensitive content, or toxic content that was publicly shared on the Internet.

Considerations for Using the Data

Social Impact of Dataset

This dataset provides data for training machine learning systems for many languages.

Discussion of Biases

Biases in the data have not been specifically studied, however as the original source of data is World Wide Web it is likely that the data has biases similar to those prevalent in the Internet. The data may also exhibit biases introduced by language identification and data filtering techniques; lower resource languages generally have lower accuracy.

Other Known Limitations

Some of the translations are in fact machine translations. While some website machine translation tools are identifiable from HTML source, these tools were not filtered out en mass because raw HTML was not available from some sources and CommonCrawl processing started from WET files.

Additional Information

Dataset Curators

The data was not curated.

Licensing Information

The dataset is released under the terms of MIT. PLEASE, USE DATA RESPONSIBLY

Citation Information

Seamless Communication et al, Seamless: Multilingual Expressive and Streaming Speech Translation. arXiv Seamless: Multilingual Expressive and Streaming Speech Translation, 2023.
Duquenne et al, SONAR EXPRESSIVE: Zero-shot Expressive Speech-to-Speech Translation. https://ai.meta.com/research/publications/sonar-expressive-zero-shot-expressive-speech-to-speech-translation/, 2023

Contributions

We thank the Seamless Communication Meta AI team for open sourcing the meta data and instructions on how to use it with special thanks to Loïc Barrault, Yu-An Chung, Mariano Coria Meglioli, David Dale, Ning Dong, Mark Duppenthaler, Paul-Ambroise Duquenne, Brian Ellis, Hady Elsahar, Justin Haaheim, John Hoffman, Min-Jae Hwang, Hirofumi Inaguma, Christopher Klaiber, Ilia Kulikov, Pengwei Li, Daniel Licht, Jean Maillard, Ruslan Mavlyutov, Alice Rakotoarison, Kaushik Ram Sadagopan, Abinesh Ramakrishnan, Tuan Tran, Guillaume Wenzek, Yilin Yang, Ethan Ye, Ivan Evtimov, Pierre Fernandez, Cynthia Gao, Prangthip Hansanti, Elahe Kalbassi, Amanda Kallet, Artyom Kozhevnikov, Gabriel Mejia Gonzalez, Robin San Roman, Christophe Touret, Corinne Wong, Carleigh Wood, Bokai Yu, Pierre Andrews, Can Balioglu, Peng-Jen Chen, Marta R. Costa-jussà, Maha Elbayad, Hongyu Gong, Francisco Guzmán, Kevin Heffernan, Somya Jain, Justine Kao, Ann Lee, Xutai Ma, Alex Mourachko, Benjamin Peloquin, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Anna Sun, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang, Mary Williamson. We also thank the Center for Language and Speech Processing(CLSP) for hosting and releasing this data, including Bismarck Bamfo Odoom and Philipp Koehn (for engineering efforts to host the data, and releasing the huggingface dataset), and Alexandre Mourachko (for organizing the connection).