LLM Reference
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.

Models ranked

2

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 provenanceScore
1
Granite Speech 4.1 2B
Version: avg-11-datasetsHarness: Not recordedEvaluator: Not recordedObserved: Apr 30, 2026Confidence: Not recordedSource
5.3
2
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. Use scores for directional filtering and shortlisting, not universal quality ranking; then validate pricing, context window, provider availability, and fit for your workload.

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.

Related benchmarks

Last reviewed: Jun 7, 2026