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MMS-1B-all
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MMS-1B-all is an open-weights speech-to-text model from Meta, released May 2023. Its published size is 0.96B parameters. Licence: cc-by-nc-4.0.
Catalogue identity notes
Exact checkpoint facebook/mms-1b-all (default English adapter, as the card's example). Weights CC-BY-NC-4.0. Params: HF safetensors total. released: HF repo creation month.
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 MMS-1B-all, out of 176 with a score, then more from Meta.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 160 |  Moonshine Streaming Tiny | 28±3 | local | ||
| 161 |  Whisper tiny.en | 27±3 | local | ||
| 162 | MMS-1B-all | 25±3 | local | ||
| 163 | Whisper base | 25±3 | local | ||
| 164 | wav2vec 2.0 Large 960h LV-60 self-training | 16±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
other tests7 not in a task · 96% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Open ASR English short-form WER (March 2026) | 22.5% ✎ | ▁▁▁▃··▁▁▂██▆ beats 10/86 | 38% |
| Danish ASR Leaderboard · mean WER over 5 Danish test sets (RyeAI) | 39.1% ✎ | ▂▂···▃▃▅█▃▂▂ beats 4/16 | 15% |
| Betthupferl (German dialect ASR benchmark) · Standard German audio → Standard German transcript WER | 29.8% ✎ | ▄·█·▄▄▄▄·█▄▄ beats 2/10 | 11% |
| Open Universal Arabic ASR Leaderboard · 6-test-set average WER | 54.5% ✎ | ▃·▂▂▅█▅▃▂▅▂▂ beats 1/22 | 11% |
| TurkASR-Bench (7 Turkish sources, 5,200 utterances) · corpus WER | 31.5% ✎ | ▃··▃▃··▅··█▅ beats 2/9 | 9.1% |
| Quranic ASR Leaderboard · held-out recitation (600 clips) overall WER | 45.5% ✎ | ▃·▅·▅▃▃·▃▃▃█ beats 0/12 | 6.8% |
| Betthupferl (German dialect ASR benchmark) · dialect audio → Standard German transcript WER | 68.0% ✎ | ▃█····▃▅▃·▅▃ beats 1/10 | 4.9% |
$ where to run it
Other ways to run it
- Run it as published · needs ~5.11 GB · from huggingface.co/facebook
uv run --with transformers --with torch python -c "from transformers import pipeline; print(pipeline('automatic-speech-recognition', model='facebook/mms-1b-all', chunk_length_s=30)('audio.wav')['text'])"
Weights and documentation: huggingface.co/facebook ↗
$ how the score was worked out · Open ASR Leaderboard 38%, German dialect ASR Benchmark… 16%, Danish Speech-to-Text Leaderb… 15% and 3 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 | 38% |
| German dialect ASR Benchmark (Betthupferl; kamaal-08/bavarian-asr-leaderboard) | 2 | 16% |
| Danish Speech-to-Text Leaderboard (RyeAI) · results dataset | 1 | 15% |
| Open Universal Arabic ASR Leaderboard (Elm Research Center) | 1 | 11% |
| Turkish ASR Leaderboard (TurkASR-Bench, yagmurtuncer) | 1 | 9% |
| Quranic ASR Leaderboard (Quran-Lab, held-out recitation set) | 1 | 7% |
| prior · pull toward the middle | 4% | |
| weighted average | = 25.3 |
$ 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