Arabic is spoken by over 300 million people across roughly 30 recognized dialect varieties, yet it has been chronically underserved by state-of-the-art speech AI. Most existing models flatten dialectal speech into Modern Standard Arabic (MSA), the formal written register, and stumble badly on the bilingual Arabic-English mixing that is common in professional settings. Cohere is taking a direct shot at that gap with Cohere Transcribe Arabic, a new open-source ASR model that now sits at the top of the Arabic speech recognition leaderboard.
What just shipped
Cohere Transcribe Arabic is available under the Apache 2.0 license. Developers can download the weights and read quickstart implementations on Hugging Face, or access the hosted model through the Cohere API or Model Vault. The API tier includes free access subject to rate limits; managed production deployments without rate limits are available through Model Vault, priced per instance-hour with discounts for longer commitments.
The numbers that matter
Cohere Transcribe Arabic achieves the lowest average word error rate (WER) of any open-source model on the Hugging Face Arabic ASR Leaderboard, with a WER of 25.87. This is a 2.45-point improvement over the previous leader, Meta's OmniASR-LLM-7B, and an 11-point improvement over OpenAI's Whisper Large V3. To put that in perspective, OmniASR-LLM-7B is a 7-billion-parameter model; Cohere Transcribe Arabic achieves better accuracy at 2 billion parameters.
Here is how the top models compare across the six benchmark datasets:
| Model | Avg WER | SADA | Common Voice | MASC clean | MASC noisy | Casablanca |
|---|---|---|---|---|---|---|
| Cohere Transcribe Arabic | 25.87 | 37.47 | 5.82 | 15.54 | 27.07 | 49.71 |
| OmniASR-LLM-7B | 28.32 | 41.61 | 9.75 | 19.69 | 29.29 | 56.46 |
| Cohere Transcribe (prev.) | 30.67 | 60.11 | 8.17 | 8.66 | 19.01 | 62.71 |
| Whisper Large v3 | 36.86 | 55.96 | 17.83 | 24.66 | 34.63 | 71.81 |
The gains are broad-based. Cohere Transcribe Arabic delivers the best overall WER and ranks first on four of the six composite task sets. On Casablanca, which evaluates conversational Arabic across eight dialects, it improves on OmniASR by nearly six points.
Human evaluation backed up the automated metrics. In head-to-head evaluations, Cohere Transcribe Arabic was preferred over Whisper in 95.8% of tests. Evaluators scored on three dimensions: overall accuracy, dialect faithfulness (whether the model preserved the speaker's regional variety rather than collapsing it into MSA), and robustness to code-switching.
Don't miss what's next in AI
Join 300,000+ engineers and researchers who get the signal, not the noise.
- Full access to in-depth AI research breakdowns
- Be the first to know what's trending before it hits mainstream
- Daily curated papers, repos, and industry moves