Open ASR Leaderboard
Phi-4-multimodal 6.14% Fading Speech recognition models transcribe the test sets of several English and multilingual corpora. Scored by average word error rate, with speed reported next to it.
Built on the ESB test sets. ASR model cards often report its average WER.
In the authors’ words
We present the Open ASR Leaderboard, a reproducible benchmarking platform with community contributions from academia and industry. It compares 86 open-source and proprietary systems across 12 datasets, with English short- and long-form and multilingual short-form tracks. We standardize word error rate (WER) and inverse real-time factor (RTFx) evaluation for consistent accuracy-efficiency comparisons across model architectures and toolkits (e.g., ESPNet, NeMo, SpeechBrain, Transformers). We observe that Conformer-based encoders paired with transformer-based decoders achieve the best average WER, while connectionist temporal classification (CTC) and token-and-duration transducer (TDT) decoders offer superior RTFx, making them better suited for long-form and batched processing. All code and dataset loaders are open-sourced to support transparent, extensible evaluation. We present our evaluation methodology to facilitate community-driven benchmarking in ASR and other tasks.
- Level
- Everyday Most adults, without training
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Short answer A number, word or short string matched to a key.
Phi-4-multimodal leads with 6.14%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 1 Phi-4-multimodal 6.14%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Phi-4-multimodal | 6.14% | Mar 2025 | Model card |
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Labs headline it less often now, last in Mar 2026
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 26 Mar 2026 |  Cohere Transcribe | 5.42 | Hugging Face Open ASR Leaderboard average WER, as of 2026-03-26 |
3 system cards report it, from 2 labs
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 23 Oct 2026 |  Nemotron 3.5 Bolt E4B technical report | 9.28 | OpenASR (WER), lower is better, non-reasoning (Table 8) | Capabilities |
| 27 Apr 2026 | Nemotron 3 Nano Omni 30B-A3B technical report | 5.95 | OpenASR Avg WER, English subset, lower is better, reasoning off (Table 8). AMI 11.09, Earnings22 11.27, GigaSpeech 9.66, LibriSpeech clean 1.57, other 2.96, SPGISpeech 1.98, TED-LIUM 3.44, VoxPopuli 5.6 | Capabilities |
| 3 Mar 2025 | Phi-4-multimodal technical report | 6.14 | OpenASR, average WER over 8 sets, lower is better (Tables 3 and 4) | Capabilities |
More in Audio and video
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- MLVU In use Multi-task long video understanding (minutes to hours) with multiple-choice and generation tasks. Accuracy. Qwen 3.8 Max 90.8%
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- TVBench In use Models answer multiple-choice questions on 10 temporal video tasks, such as action counting and ordering, that static frames cannot solve. Scored by accuracy. Qwen 3.8 Max 81.9%