MOSS Transcribe Diarize
OpenMOSS · released May 19, 2026 · OpenMOSS-Team/MOSS-Transcribe-Diarize
Speech to textTranscribes a recording into words
- Type
- Open weightsApache License 2.0
- Languages
- 2
- Size
- 0.9B
Context measured in tokens
Our take
Written Aug 2, 2026MOSS Transcribe Diarize is a sub-1-billion-parameter speech-to-text model from OpenMOSS that identifies who is speaking while it transcribes. It processes audio extremely fast under a permissive Apache licence, but you must run it yourself and it only supports English and Chinese.
Pick this when you need speaker diarization built into transcription, or when permissive open-source licensing matters for compliance. Use it for throughput-heavy pipelines where speed beats perfection — it processes an hour of audio in roughly nine seconds. Skip it if you need commercial hosting, languages beyond English and Chinese, or consistently high accuracy on messy audio like meetings and accented speech.
The case for it
- Extremely fast: processes one hour of audio in approximately nine seconds at 382.37 times real time.
- Permissive Apache 2.0 licence allows commercial use, modification and redistribution.
- Strong on clean prepared speech, with low error rates on read-aloud audio and financial calls.
- Handles European-accented speech notably better than general accented speech.
The case against it
- Accuracy degrades sharply in challenging conditions: error rate climbs roughly fivefold on recorded meetings and general accented speech versus clean speech.
- No commercial hosting available in our data; you must self-host.
- Only two languages listed, and every accuracy figure we hold is English-only — nothing measured for Chinese or any other language.
How good is it?
TranscriptionTurning speech into text4 of 5Open ASR WER · 20th of 74
94.8%
Misses roughly one word in 19, averaged over nine English test sets.
382×44th of 62
an hour of audio in 9 seconds, on the board's own hardware. Your machine will differ.
2
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
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. 20.8 GB spare means a 10% error in the size would not change the answer.
Apple M1 Pro (16-core GPU) · 32 GB
Room to spare. 22 GB spare means a 10% error in the size would not change the answer.
Apple M2 (8-core GPU, 8GB unified) · 8 GB
Room to spare. 4 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 allowsApache License 2.0, 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
Apache License 2.0
Fully permissive: commercial use, redistribution, and derivatives allowed. Requires attribution and a copy of the license. Includes an express patent grant.
Identifiers
- Hugging Face
- OpenMOSS-Team/MOSS-Transcribe-Diarize
- Architecture
- Dense
- Modality record
- audio->text
- Catalogue slug
- openmoss-team-moss-transcribe-diarize