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 assist to hundreds extra. Via a characteristic referred to as zero-shot in-context studying, customers can present a number of paired examples of audio and textual content in a brand new language at inference time, enabling the mannequin to transcribe further utterances in that language with none retraining.
In apply, this expands potential protection to greater than 5,400 languages — roughly each spoken language with a identified 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 up to now.
Better of all: it's been open sourced below 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 except they paid licensing charges — that means researchers and builders are free to take and implement it straight away, totally free, with out restrictions, even in business and enterprise-grade initiatives!
Launched on November 10 on Meta's web site, Github, together with a demo house 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 a large speech corpus spanning over 350 beforehand underserved languages.
All sources are freely accessible below open licenses, and the fashions assist speech-to-text transcription out of the field.
“By open sourcing these fashions and dataset, we purpose to interrupt down language limitations, develop digital entry, 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 information, Omnilingual ASR features a zero-shot variant.
This model can transcribe languages it has by no means seen earlier than—utilizing only a few 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 contains a number of mannequin households educated on greater than 4.3 million hours of audio from 1,600+ languages:
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wav2vec 2.0 fashions for self-supervised speech illustration studying (300M–7B parameters)
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CTC-based ASR fashions for environment friendly supervised transcription
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LLM-ASR fashions combining a speech encoder with a Transformer-based textual content decoder for state-of-the-art transcription
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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 related fashions have superior ASR capabilities for world languages, they fall brief on the lengthy tail of human linguistic variety. Whisper helps 99 languages. Meta’s system:
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Straight helps 1,600+ languages
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Can generalize to five,400+ languages utilizing in-context studying
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Achieves character error charges (CER) below 10% in 78% of supported languages
Amongst these supported are greater than 500 languages by no means beforehand lined by any ASR mannequin, in accordance with Meta’s analysis paper.
This growth opens new potentialities 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 adjustments, 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 yr marked by organizational turbulence, management adjustments, and uneven product execution.
Omnilingual ASR is the primary main open-source mannequin launch for the reason that rollout of Llama 4, Meta’s newest giant language mannequin, which debuted in April 2025 to blended and finally poor critiques, 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 information provider Scale AI, as Chief AI Officer, and embark on an intensive and expensive 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 site the place the corporate has traditionally led — multilingual AI — and gives a very extensible, community-oriented stack with minimal limitations to entry.
The system’s assist 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 expertise.
Importantly, it does so by way of a free and permissively licensed launch, below 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 “private 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 information 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 realize 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:
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African Subsequent Voices: A Gates Basis–supported consortium together with Maseno College (Kenya), College of Pretoria, and Information Science Nigeria
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Mozilla Basis’s Widespread Voice, supported by way of the Open Multilingual Speech Fund
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Lanfrica / NaijaVoices, which created information for 11 African languages together with Igala, Serer, and Urhobo
The info assortment centered on pure, unscripted speech. Prompts had been designed to be culturally related and open-ended, comparable to “Is it higher to have a number of shut mates or many informal acquaintances? Why?” Transcriptions used established writing methods, with high quality assurance constructed into each step.
Efficiency and {Hardware} Concerns
The most important 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 eventualities:
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CER <10% in 95% of high-resource and mid-resource languages
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CER <10% in 36% of low-resource languages
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Robustness in noisy situations and unseen domains, particularly with fine-tuning
The zero-shot system, omniASR_LLM_7B_ZS, can transcribe new languages with minimal setup. Customers present a number of pattern audio–textual content pairs, and the mannequin generates transcriptions for brand new utterances in the identical language.
Open Entry and Developer Tooling
All fashions and the dataset are licensed below permissive phrases:
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Apache 2.0 for fashions and code
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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:
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A HuggingFace dataset integration
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Pre-built inference pipelines
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Language-code conditioning for improved accuracy
Builders can view the total 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 set checklist to an extensible framework. It allows:
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Neighborhood-driven inclusion of underrepresented languages
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Digital entry for oral and endangered languages
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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 mannequin can ever anticipate and embrace all the world’s languages upfront,” the Omnilingual ASR paper states, “however Omnilingual ASR makes it potential for communities to increase recognition with their very own information.”
Entry the Instruments
All sources are actually accessible at:
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Code + Fashions: github.com/facebookresearch/omnilingual-asr
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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 business ASR APIs that assist 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 very helpful for enterprises working in sectors like voice-based buyer assist, transcription companies, accessibility, training, or civic expertise, the place native language protection generally is a aggressive or regulatory necessity. As a result of the fashions are launched below 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 slightly than linguistic limitation.
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