  We introduce FLEURS, the Few-shot Learning Evaluation of Universal
Representations of Speech benchmark. FLEURS is an n-way parallel speech dataset
in 102 languages built on top of the machine translation FLoRes-101 benchmark,
with approximately 12 hours of speech supervision per language. FLEURS can be
used for a variety of speech tasks, including Automatic Speech Recognition
(ASR), Speech Language Identification (Speech LangID), Translation and
Retrieval. In this paper, we provide baselines for the tasks based on
multilingual pre-trained models like mSLAM. The goal of FLEURS is to enable
speech technology in more languages and catalyze research in low-resource
speech understanding.
