Meta has simply launched a brand new multilingual computerized speech recognition (ASR) system supporting 1,600+ languages — dwarfing OpenAI’s open supply Whisper mannequin, which helps simply 99.
Is structure additionally permits builders to increase that help to hundreds extra. By way of a function known as zero-shot in-context studying, customers can present just a few paired examples of audio and textual content in a brand new language at inference time, enabling the mannequin to transcribe extra utterances in that language with none retraining.
In observe, this expands potential protection to greater than 5,400 languages — roughly each spoken language with a recognized script.
It’s a shift from static mannequin capabilities to a versatile framework that communities can adapt themselves. So whereas the 1,600 languages replicate official coaching protection, the broader determine represents Omnilingual ASR’s capability to generalize on demand, making it essentially the most extensible speech recognition system launched so far.
Better of all: it's been open sourced beneath a plain Apache 2.0 license — not a restrictive, quasi open-source Llama license like the corporate's prior releases, which restricted use by bigger enterprises until they paid licensing charges — that means researchers and builders are free to take and implement it instantly, totally free, with out restrictions, even in industrial and enterprise-grade initiatives!
Launched on November 10 on Meta's web site, Github, together with a demo area on Hugging Face and technical paper, Meta’s Omnilingual ASR suite features a household of speech recognition fashions, a 7-billion parameter multilingual audio illustration mannequin, and an enormous speech corpus spanning over 350 beforehand underserved languages.
All sources are freely obtainable beneath open licenses, and the fashions help speech-to-text transcription out of the field.
“By open sourcing these models and dataset, we aim to break down language barriers, expand digital access, and empower communities worldwide,” Meta posted on its @AIatMeta account on X
Designed for Speech-to-Textual content Transcription
At its core, Omnilingual ASR is a speech-to-text system.
The fashions are educated to transform spoken language into written textual content, supporting functions like voice assistants, transcription instruments, subtitles, oral archive digitization, and accessibility options for low-resource languages.
In contrast to earlier ASR fashions that required intensive labeled coaching knowledge, Omnilingual ASR features a zero-shot variant.
This model can transcribe languages it has by no means seen earlier than—utilizing just some paired examples of audio and corresponding textual content.
This lowers the barrier for including new or endangered languages dramatically, eradicating the necessity for big corpora or retraining.
Mannequin Household and Technical Design
The Omnilingual ASR suite consists of a number of mannequin households educated on greater than 4.3 million hours of audio from 1,600+ languages:
wav2vec 2.0 fashions for self-supervised speech illustration studying (300M–7B parameters)
CTC-based ASR fashions for environment friendly supervised transcription
LLM-ASR fashions combining a speech encoder with a Transformer-based textual content decoder for state-of-the-art transcription
LLM-ZeroShot ASR mannequin, enabling inference-time adaptation to unseen languages
All fashions comply with an encoder–decoder design: uncooked audio is transformed right into a language-agnostic illustration, then decoded into written textual content.
Why the Scale Issues
Whereas Whisper and comparable fashions have superior ASR capabilities for international languages, they fall brief on the lengthy tail of human linguistic range. Whisper helps 99 languages. Meta’s system:
Immediately helps 1,600+ languages
Can generalize to five,400+ languages utilizing in-context studying
Achieves character error charges (CER) beneath 10% in 78% of supported languages
Amongst these supported are greater than 500 languages by no means beforehand lined by any ASR mannequin, in keeping with Meta’s analysis paper.
This growth opens new prospects for communities whose languages are sometimes excluded from digital instruments
Right here’s the revised and expanded background part, integrating the broader context of Meta’s 2025 AI technique, management modifications, and Llama 4’s reception, full with in-text citations and hyperlinks:
Background: Meta’s AI Overhaul and a Rebound from Llama 4
The discharge of Omnilingual ASR arrives at a pivotal second in Meta’s AI technique, following a 12 months marked by organizational turbulence, management modifications, and uneven product execution.
Omnilingual ASR is the primary main open-source mannequin launch because the rollout of Llama 4, Meta’s newest giant language mannequin, which debuted in April 2025 to combined and finally poor opinions, with scant enterprise adoption in comparison with Chinese language open supply mannequin rivals.
The failure led Meta founder and CEO Mark Zuckerberg to nominate Alexandr Wang, co-founder and prior CEO of AI knowledge provider Scale AI, as Chief AI Officer, and embark on an intensive and dear hiring spree that shocked the AI and enterprise communities with eye-watering pay packages for high AI researchers.
In distinction, Omnilingual ASR represents a strategic and reputational reset. It returns Meta to a website the place the corporate has traditionally led — multilingual AI — and affords a very extensible, community-oriented stack with minimal limitations to entry.
The system’s help for 1,600+ languages and its extensibility to over 5,000 extra by way of zero-shot in-context studying reassert Meta’s engineering credibility in language know-how.
Importantly, it does so via a free and permissively licensed launch, beneath Apache 2.0, with clear dataset sourcing and reproducible coaching protocols.
This shift aligns with broader themes in Meta’s 2025 technique. The corporate has refocused its narrative round a “personal superintelligence” imaginative and prescient, investing closely in infrastructure (together with a September launch of customized AI accelerators and Arm-based inference stacks) supply whereas downplaying the metaverse in favor of foundational AI capabilities. The return to public coaching knowledge in Europe after a regulatory pause additionally underscores its intention to compete globally, regardless of privateness scrutiny supply.
Omnilingual ASR, then, is greater than a mannequin launch — it’s a calculated transfer to reassert management of the narrative: from the fragmented rollout of Llama 4 to a high-utility, research-grounded contribution that aligns with Meta’s long-term AI platform technique.
Neighborhood-Centered Dataset Assortment
To attain this scale, Meta partnered with researchers and neighborhood organizations in Africa, Asia, and elsewhere to create the Omnilingual ASR Corpus, a 3,350-hour dataset throughout 348 low-resource languages. Contributors had been compensated native audio system, and recordings had been gathered in collaboration with teams like:
African Subsequent Voices: A Gates Basis–supported consortium together with Maseno College (Kenya), College of Pretoria, and Information Science Nigeria
Mozilla Basis’s Frequent Voice, supported via the Open Multilingual Speech Fund
Lanfrica / NaijaVoices, which created knowledge for 11 African languages together with Igala, Serer, and Urhobo
The info assortment targeted on pure, unscripted speech. Prompts had been designed to be culturally related and open-ended, comparable to “Is it better to have a few close friends or many casual acquaintances? Why?” Transcriptions used established writing methods, with high quality assurance constructed into each step.
Efficiency and {Hardware} Issues
The biggest mannequin within the suite, the omniASR_LLM_7B, requires ~17GB of GPU reminiscence for inference, making it appropriate for deployment on high-end {hardware}. Smaller fashions (300M–1B) can run on lower-power units and ship real-time transcription speeds.
Efficiency benchmarks present robust outcomes even in low-resource situations:
CER <10% in 95% of high-resource and mid-resource languages
CER <10% in 36% of low-resource languages
Robustness in noisy circumstances and unseen domains, particularly with fine-tuning
The zero-shot system, omniASR_LLM_7B_ZS, can transcribe new languages with minimal setup. Customers present just a few pattern audio–textual content pairs, and the mannequin generates transcriptions for brand spanking new utterances in the identical language.
Open Entry and Developer Tooling
All fashions and the dataset are licensed beneath permissive phrases:
Apache 2.0 for fashions and code
CC-BY 4.0 for the Omnilingual ASR Corpus on HuggingFace
Set up is supported by way of PyPI and uv:
pip set up omnilingual-asr
Meta additionally gives:
A HuggingFace dataset integration
Pre-built inference pipelines
Language-code conditioning for improved accuracy
Builders can view the complete checklist of supported languages utilizing the API:
from omnilingual_asr.fashions.wav2vec2_llama.lang_ids import supported_langs
print(len(supported_langs))
print(supported_langs)
Broader Implications
Omnilingual ASR reframes language protection in ASR from a hard and fast checklist to an extensible framework. It permits:
Neighborhood-driven inclusion of underrepresented languages
Digital entry for oral and endangered languages
Analysis on speech tech in linguistically numerous contexts
Crucially, Meta emphasizes moral issues all through—advocating for open-source participation and collaboration with native-speaking communities.
“No model can ever anticipate and include all of the world’s languages in advance,” the Omnilingual ASR paper states, “but Omnilingual ASR makes it possible for communities to extend recognition with their own data.”
Entry the Instruments
All sources are actually obtainable at:
Code + Fashions: github.com/facebookresearch/omnilingual-asr
Dataset: huggingface.co/datasets/fb/omnilingual-asr-corpus
Blogpost: ai.meta.com/weblog/omnilingual-asr
What This Means for Enterprises
For enterprise builders, particularly these working in multilingual or worldwide markets, Omnilingual ASR considerably lowers the barrier to deploying speech-to-text methods throughout a broader vary of consumers and geographies.
As an alternative of counting on industrial ASR APIs that help solely a slim set of high-resource languages, groups can now combine an open-source pipeline that covers over 1,600 languages out of the field—with the choice to increase it to hundreds extra by way of zero-shot studying.
This flexibility is particularly useful for enterprises working in sectors like voice-based buyer help, transcription providers, accessibility, schooling, or civic know-how, the place native language protection is usually a aggressive or regulatory necessity. As a result of the fashions are launched beneath the permissive Apache 2.0 license, companies can fine-tune, deploy, or combine them into proprietary methods with out restrictive phrases.
It additionally represents a shift within the ASR panorama—from centralized, cloud-gated choices to community-extendable infrastructure. By making multilingual speech recognition extra accessible, customizable, and cost-effective, Omnilingual ASR opens the door to a brand new technology of enterprise speech functions constructed round linguistic inclusion relatively than linguistic limitation.
