Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages

Abstract

While automatic speech recognition (ASR) systems have made remarkable progress in many high resource languages, most of the world’s 7,000+ languages remain unsupported, with thousands of long-tail languages effectively left behind. Expanding ASR coverage has long been regarded as prohibitively expensive and of limited benchmark value, further hampered by architectures that restrict language coverage to a fixed set that make extension inaccessible to most communities—all while entangled with ethical concerns when pursued without community collaboration. To transcend these limitations, this article introduces Omnilingual ASR, the first large-scale ASR system designed for extensibility. More specifically, Omnilingual ASR enables communities to introduce unserved languages with only a handful of their own data samples. On the modeling side, Omnilingual ASR scales self-supervised pre-training to 7B parameters to learn robust speech representations and introduces an encoder–decoder architecture designed for zero-shot generalization, leveraging a large language model-inspired decoder to effectively exploit these representations. This capability is grounded in a massive and diverse training corpus; by combining breadth of coverage with linguistic variety, the model learns representations robust enough to adapt to previously unseen languages. Incorporating public resources with community-sourced recordings gathered through compensated local partnerships, Omnilingual ASR expands coverage to more than 1,600 languages, the largest such effort to date—including over 500 never before served by any ASR system. Automatic evaluations show substantial gains over prior systems, especially in extreme low-resource conditions, and strong generalization to languages never encountered during training. Crucially, Omnilingual ASR is released as a family of models ranging from compact 300M variants for low-power devices to large 7B models for maximum accuracy. Throughout the paper, we reflect on the ethical considerations shaping this design and conclude by discussing its broader societal impact. In particular, we highlight how open-sourcing models and tools can lower barriers for researchers and communities alike, inviting new forms of participation without requiring onerous expertise or heavy compute. All open-source artifacts from this effort are available at https://github.com/facebookresearch/omnilingual-asr.