Open ASR: Open ASR Leaderboard (average WER)
About
Average Word Error Rate across 11 diverse English test sets on the HuggingFace Open ASR Leaderboard (hf-audio). Covers read speech, earnings calls, meetings, TED talks, and parliamentary speeches. More representative of real-world deployment than single-dataset benchmarks. Lower is better.Compare model scores alongside pricing, API availability, and release date — high benchmark score alone doesn't make a model the right pick.
How to read this benchmark
This benchmark uses a scoring system where lower is better. Scores are useful for directional filtering and model shortlisting, not for universal quality ranking.
Interpretation checklist: prefer benchmarks that are closest to your workload style, then validate against the linked model pages for pricing, context window, and provider availability.
Trust when:
- There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
- Model list covers the same version family you can actually deploy today.
- Top candidates overlap with your required routing and feature requirements.
Don't trust when:
- There is only one benchmark snapshot or the dataset appears stale.
- Benchmark metric direction is opposite of your decision objective.
- The score difference between options is narrow and likely within implementation variance.
Current modeled score band for tracked entries is roughly 5.4 – 5.3.
Window
Mar 7 to Apr 30
last 3 snapshots
Mean delta
-0.09
score points
Coverage
1
models in latest snapshot
Leaderboard preview (top 5)
- 1. Transcribe (03-2026)5.4
- 2. Granite Speech 4.1 2B5.3