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BibleMMS: massively multilingual TTS dataset

The Dataset associated with the Paper "Meta Learning Text-to-Speech Synthesis in over 7000 Languages" by Florian Lux, Sarina Meyer, Lyonel Behringer, Frank Zalkow, Phat Do, Matt Coler, Emanuël A. P. Habets and Ngoc Thang Vu (Interspeech 2024). We generate 2000 spo...

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A massively parallel corpus: the Bible in 100 languages

We describe the creation of a massively parallel corpus based on 100 translations of the Bible. We discuss some of the difficulties in acquiring and processing the raw material as well as the potential of the Bible as a corpus for natural language processing. Final...

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AfroLID: A Neural Language Identification Tool for African Languages

Language identification (LID) is a crucial precursor for NLP, especially for mining web data. Problematically, most of the world's 7000+ languages today are not covered by LID technologies. We address this pressing issue for Africa by introducing AfroLID, a neural ...

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AfroLID: A Neural Language Identification Tool for African Languages

AfroLID is a powerful neural toolkit for African languages identification which covers 517 African languages....

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Bible Para

This is a multilingual parallel corpus created from translations of the Bible compiled by Christos Christodoulopoulos and Mark Steedman. 102 languages, 5,148 bitexts total number of files: 107 total number of tokens: 56.43M total number of sentence fragments: 2.84...

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CMU Wilderness Multilingual Speech Dataset

A dataset of over 700 different languages providing audio, aligned text and word pronunciations. On average each language provides around 20 hours of sentence-lengthed transcriptions. Data is mined from read New Testaments from http://www.bible.is/

CMU Wilderness Multilingual Speech Dataset (Paper)

This paper describes the CMU Wilderness Multilingual Speech Dataset. A dataset of over 700 different languages providing audio, aligned text and word pronunciations. On average each language provides around 20 hours of sentence-lengthed transcriptions. We describe ...

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Meta Learning Text-to-Speech Synthesis in over 7000 Languages

In this work, we take on the challenging task of building a single text-to-speech synthesis system that is capable of generating speech in over 7000 languages, many of which lack sufficient data for traditional TTS development. By leveraging a novel integration of ...

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NLLB-CLIP -- train performant multilingual image retrieval model on a budget

Today, the exponential rise of large models developed by academic and industrial institutions with the help of massive computing resources raises the question of whether someone without access to such resources can make a valuable scientific contribution. To explor...

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NLLB: No Language Left Behind

No Language Left Behind (NLLB) is a first-of-its-kind, AI breakthrough project that open-sources models capable of delivering high-quality translations directly between any pair of 200+ languages — including low-resource languages like Asturian, Luganda, Urdu and m...

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No Language Left Behind : Data

NLLB project uses data from three sources : public bitext, mined bitext and data generated using backtranslation. Details of different datasets used and open source links are provided in details here.

No Language Left Behind: Scaling Human-Centered Machine Translation

Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset of languages, leaving behind the va...

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PanLex: Building a Resource for Panlingual Lexical Translation

PanLex, a project of The Long Now Foundation, aims to enable the translation of lexemes among all human languages in the world. By focusing on lexemic translations, rather than grammatical or corpus data, it achieves broader lexical and language coverage than relat...

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PanLex: World vocabulary listing

PanLex is a nonprofit whose mission is to overcome language barriers to human rights, information, and opportunities. We believe that nobody should have their rights restricted because of the language they speak.

SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects

Despite the progress we have recorded in the last few years in multilingual natural language processing, evaluation is typically limited to a small set of languages with available datasets which excludes a large number of low-resource languages. In this paper, we c...

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