Open ASRactiveAudio

Open ASR: Open ASR Leaderboard (average WER)

Metric: Avg WER (%) (lower is better)Introduced: 2023

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. High benchmark score alone doesn't make a model the right pick — weigh it against pricing, API availability, and release date.

Models ranked

tracked on this benchmark

Score band

5.3 – 5.4

best → lowest tracked

Snapshot trend

-0.09

Mar 7 → Apr 30 · 1 models

Leaderboard

Tracked models ranked by Avg WER (%) (lower is better).

Compare candidates

#Model variant and provenanceRelative to leaderScore

Granite Speech 4.1 2B

Version: avg-11-datasetsHarness: Not recordedEvaluator: Not recordedObserved: Apr 30, 2026Confidence: Not recordedSource

5.3

Cohere Transcribe (03-2026)

Version: avg-11-datasetsHarness: Not recordedEvaluator: Not recordedObserved: Mar 7, 2026Confidence: Not recordedSource

5.4

How to read this benchmark

This benchmark scores models where lower is better. Scores are useful for directional filtering and shortlisting — not for universal quality ranking. Prefer benchmarks closest to your workload, then validate the linked model pages for pricing, context window, and provider availability.

Trust this score when

There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
The model list covers the same version family you can actually deploy today.
Top candidates overlap with your required routing and feature requirements.

Be cautious when

There is only one benchmark snapshot or the dataset appears stale.
The benchmark metric direction is opposite of your decision objective.
The score difference between options is narrow and likely within implementation variance.

FAQ

What does the Open ASR benchmark measure?

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. On this page it lists 2 tracked model variants where lower is better.

Is a higher Open ASR Leaderboard (average WER) score always better?

For this benchmark, lower is better. A high score helps you shortlist, but confirm pricing, context window, and provider availability on each model page before committing — the top scorer is not always the right pick for your workload or budget.

How current is this Open ASR Leaderboard (average WER) data?

This benchmark was last reviewed on Jun 7, 2026. The tracked score average moved -0.09 points across the last 2 snapshots.