ASR Conformer Largescaleasr
SpeechBrain · released Feb 6, 2025 · speechbrain/asr-conformer-largescaleasr
Speech to textTranscribes a recording into words
- Type
- Open weightsApache License 2.0
- Languages
- 1
- Size
- 0.5B
Context measured in tokens
Our take
Written Aug 3, 2026ASR Conformer Largescaleasr is a compact English-only speech-to-text model from SpeechBrain that processes audio extremely fast on benchmark hardware. Its Apache licence and small footprint suit self-hosted deployment, though accuracy falls sharply on messy real-world recordings and no commercial hosts currently offer it.
Pick this for fast batch transcription of clean English audio where you can self-host — it processes an hour of audio in roughly 50 seconds and gets under 2% word error on read-aloud material. Use it for financial calls or European-accented speech at similar error rates. Skip it if your audio is from meetings, podcasts or heavily accented speakers, or if you need languages other than English.
The case for it
- Extremely fast for its size class: processes an hour of audio in approximately 50 seconds on benchmark hardware.
- Strong on clean, structured English audio, with under 2% word error on read-aloud material and under 4% on financial calls.
- Fully open under Apache 2.0, with no cloud dependency required.
- Compact at 0.5 billion parameters as listed.
The case against it
- Accuracy collapses on challenging audio: error rate jumps to 15.57% on recorded meetings, more than eight times worse than on clean read speech.
- English only; no measured data for any other language.
- Zero commercial offers currently tracked, so self-hosting is the only path.
How good is it?
TranscriptionTurning speech into text1.5 of 5Open ASR WER · 65th of 74
92.4%
Misses roughly one word in 13, averaged over nine English test sets.
72.4×61st of 62
an hour of audio in 50 seconds, on the board's own hardware. Your machine will differ.
1
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.
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. 21.3 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. 22.5 GB spare means a 10% error in the size would not change the answer.
Apple M1 (8-core GPU, 8GB unified) · 8 GB
Room to spare. 4.5 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
- speechbrain/asr-conformer-largescaleasr
- Modality record
- audio->text
- Catalogue slug
- speechbrain-asr-conformer-largescaleasr