Omnilingual ASR

Meta FAIR introduces Omnilingual ASR, an open-source multilingual speech recognition system supporting over 1600 languages (over 500 never before included in any ASR system), using a 7B parameter encoder-decoder architecture that enables zero-shot generalization to new languages and dialects with just a few training examples. - Massive-scale self-supervised learning: Built on wav2vec 2.0 architecture scaled to 7B parameters, the largest self-supervised speech model to date. The encoder-decoder design enables zero-shot transfer to languages never seen during training, released as a model family to support different deployment scenarios. - Community-sourced training corpus: Assembled 4.3M hours of speech across 1,239 languages by combining public resources with the commissioned Omnilingual ASR Corpus. This represents the most linguistically diverse speech dataset ever created for ASR research. - Superior performance across benchmarks: Outperforms Whisper, Universal Speech Model, and Massively Multilingual Speech on FLEURS, CommonVoice, and in-house evaluation sets. Achieves particularly strong results on low-resource languages through effective knowledge transfer. - Democratizing speech technology: Open-sources all models, training code, and data collection protocols to enable communities to extend the system. Provides few-shot adaptation framework where communities can achieve competitive ASR performance with just 10-100 examples.