VibeVoice ASR HF
Microsoft · released Mar 2, 2026 · microsoft/VibeVoice-ASR-HF
Speech to textTranscribes a recording into words
- Type
- Open weightsMIT License
- Languages
- 51
- Size
- 8.3B
Context measured in tokens
Our take
Written Aug 2, 2026VibeVoice ASR HF is an 8-billion-parameter speech-to-text model from Microsoft with a permissive MIT licence. It turns audio into text across 51 claimed languages and is extremely fast on benchmark hardware, though its accuracy varies sharply with recording quality.
Pick this for open-source transcription with a permissive licence, or for high-throughput batch processing. Use it for clean read-aloud English audio, or multilingual deployment where breadth beats verified accuracy. Skip it if you need reliable transcription of meetings or accented speech, or if you require hosted inference.
The case for it
- Extremely fast on benchmark hardware: one hour of audio processed in roughly sixteen seconds.
- Permissive MIT licence allows commercial use, modification and redistribution.
- Strong on clean read-aloud English speech, with a word error rate well below its own overall average.
- Broad language coverage claimed, including major world languages.
The case against it
- Accuracy collapses on challenging audio: recorded meetings and accented speech both show error rates more than seven times worse than its clean-speech performance.
- No hosted inference options in our data, so self-hosting is the only path.
How good is it?
TranscriptionTurning speech into text2.5 of 5Open ASR WER · 49th of 74
93.7%
Misses roughly one word in 16, averaged over nine English test sets.
221×47th of 62
an hour of audio in 16 seconds, on the board's own hardware. Your machine will differ.
51
Listed on the model card. The accuracy above is English only.
Percentage of words wrong on each set, lower better. Bars are scaled to this model's own worst case, not to the board.
Which languages ↓ ↑
English · Chinese · Spanish · Portuguese · German · Japanese · Korean · French · Russian · Indonesian · Swedish · Italian · Hebrew · Dutch · Polish · Norwegian · Turkish · Thai · Arabic · Hungarian · Catalan · Czech · Danish · Persian · Afrikaans · Hindi · Finnish · Estonian · aa · Greek · Romanian · Vietnamese · Bulgarian · Icelandic · Slovenian · Slovak · Lithuanian · Swahili · Ukrainian · kl · Latvian · Croatian · Nepali · Serbian · Filipino · Yiddish · Malay · Urdu · Mongolian · Armenian · Javanese
The figures above come from the Open ASR Leaderboard, an independent public test that runs every model on the same recordings. It is the only measurement of transcription quality we know of, so there are no other scores to show.
These tests check whether a model follows instructions — a precondition for all the work above, but not a measure of how well that work is done, which is why they get no rating.
Every published score for this model9 scoresEvery figure we hold, from 9 boards, with who ran it and a link to the source — including the boards no rating above is built on.
Can you run it yourself?
- Fits in memory
- weights load entirely on the card
- Spills to system RAM
- some weights offload; much slower
- Too large
- will not load even with offload
- est
- size is calculated; the verdict could change by 10%
Comfortable fit
GeForce RTX 4090 · 24 GB
Room to spare. 16.1 GB spare means a 10% error in the size would not change the answer.
GeForce RTX 5090 · 32 GB
Room to spare. 24.1 GB spare means a 10% error in the size would not change the answer.
Apple M1 (8-core GPU) · 16 GB
Room to spare. 5.3 GB spare means a 10% error in the size would not change the answer.
Memory use by level
Against a 24 GB card.
All 3 sizes here are calculated, not measured. We hold no measured file for this model, so each size comes from the parameter count and every verdict above inherits that uncertainty.
Check against your own machine → · All 71 devices, with every size →
When we formed this view
Dates behind this page
Prices last checked 6h ago
What we do not know about this model yet
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
Licence and identifiers
What the licence allowsMIT License, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
MIT License
Fully permissive: do anything with attribution. No patent grant, unlike Apache-2.0.
Identifiers
- Hugging Face
- microsoft/VibeVoice-ASR-HF
- Architecture
- Dense
- Modality record
- audio->text
- Catalogue slug
- microsoft-vibevoice-asr-hf