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Omnilingual ASR LLM 1B
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Omnilingual ASR LLM 1B is an open-weights speech-to-text model from Meta, released November 2025. Its published size is 2.28B parameters. Licence: apache-2.0.
Catalogue identity notes
Exact checkpoint facebook/omniASR-LLM-1B (fairseq2 card 'omniASR_LLM_1B', first Omnilingual ASR release, Nov 2025); distinct from its '_v2' successor. Params: card table. Needs libsndfile (card).
Score within the Open ASR English short-form WER (March 2026) comparison set only.
$ how it compares
The speech-to-text models ranked either side of Omnilingual ASR LLM 1B, out of 176 with a score, then more from Meta.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 74 |  GPT-4o Mini Transcribe | 56±2 | api $0.0030/audio min | ||
| 75 | Omnilingual ASR LLM 1B v2 | 56±3 | local | ||
| 76 | Omnilingual ASR LLM 1B | 56±2 | local | ||
| 77 |  Distil-Whisper distil-large-v3 | 56±2 | local | ||
| 78 | Omnilingual ASR LLM 3B v2 | 56±3 | local | ||
| more from Meta | |||||
| 1 | Muse Voice Transcribe 1.0 | 71±3 | api $0.0030/audio min | ||
| 58 | Omnilingual ASR LLM 7B | 58±2 | local | ||
| 59 | Omnilingual ASR LLM 7B v2 | 58±3 | local | ||
see all speech-to-text models · find the ones that fit your machine
$ what it's good at
multilingual transcription▂··▃▄█▆▅▄▂·▂ #26 of 36 * · 65% of its score · 1 test
Beats Voxtral Mini Transcribe V2, GPT-4o Mini Transcribe here, though they rank higher overall. Trails Omnilingual ASR CTC 1B v2, Omnilingual ASR CTC 7B v2, SeamlessM4T v2 Large here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Open ASR multilingual WER | 6.7% ✎ | ▂··▂▃▃█▃█▂▁▁ beats 8/29 | 65% |
other tests4 not in a task · 33% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Omnilingual ASR paper · FLEURS-81 test mean CER (Whisper-covered languages) lab | 6.6% ✎ | ▅···▃···▃▃██ beats 8/10 | 14% |
| Omnilingual ASR paper · Common Voice 22 (76 languages) test mean CER lab | 6.5% ✎ | ▂▂▂····▂▆··█ beats 9/10 | 10% |
| Open Universal Arabic ASR Leaderboard · 6-test-set average WER | 30.0% ✎ | ▃·▂▂▅█▅▃▂▅▂▂ beats 19/22 | 5.6% |
| Quranic ASR Leaderboard · held-out recitation (600 clips) overall WER | 24.9% ✎ | ▃·▅·▅▃▃·▃▃▃█ beats 4/12 | 3.4% |
$ where to run it
Other ways to run it
- Run it as published · needs ~5.97 GB · from huggingface.co/facebook
uv run --python 3.12 --with omnilingual-asr python -c "from omnilingual_asr.models.inference.pipeline import ASRInferencePipeline as P; print(P(model_card='omniASR_LLM_1B').transcribe(['audio.wav'], batch_size=1))"
Weights and documentation: huggingface.co/facebook ↗
$ how the score was worked out · Open ASR Leaderboard 65%, Omnilingual ASR: Open-Source… 24%, Open Universal Arabic ASR Lea… 6% and 1 more
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 Open ASR Leaderboard | 1 | 65% |
 Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages (arXiv:2511.09690v1) · lab-reported | 2 | 24% |
| Open Universal Arabic ASR Leaderboard (Elm Research Center) | 1 | 6% |
| Quranic ASR Leaderboard (Quran-Lab, held-out recitation set) | 1 | 3% |
| prior · pull toward the middle | 2% | |
| weighted average | = 55.6 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json