Cohere Transcribe Arabic is an open-source model built for Arabic's toughest transcription problems
Quick Answer
Cohere has launched Cohere Transcribe Arabic, a 2-billion-parameter open-source ASR model designed for Arabic speech recognition.
Quick Take
Cohere has launched Cohere Transcribe Arabic, a 2-billion-parameter open-source ASR model designed for Arabic speech recognition. It surpasses Whisper Large V3 and other systems in accuracy, particularly in handling dialects, code-switching, and specialized vocabulary, making it the most precise open-source Arabic speech-to-text solution available.
Key Points
- Cohere Transcribe Arabic is the most accurate open-source Arabic ASR model.
- The model outperforms Whisper Large V3 in overall quality and dialect fidelity.
- It is designed to tackle challenges like bilingual conversations and code-switching.
- Available under Apache 2.0 license on Hugging Face and Cohere API.
- Human ratings show superior performance in dialect faithfulness and specialized vocabulary.
📖 Reader Mode
~1 min read
Jul 7, 2026
Cohere has released Cohere Transcribe Arabic, an open-source model built for Arabic speech recognition. The 2-billion-parameter ASR model is, according to Cohere, the most accurate open-source Arabic speech-to-text system available. It targets the specific challenges of Arabic speech, including dialect variety, bilingual Arabic-English conversations, code-switching, and specialized vocabulary. Cohere says it outscores Whisper Large V3, the standard Cohere Transcribe model, and other systems in benchmarks.
The model ships under the Apache 2.0 license and is available on Hugging Face and through the Cohere API. More benchmarks and examples are on the Cohere blog.
— Originally published at the-decoder.com
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from The Decoder
See more →
An AI model programmed nonstop for 19 days on a single MirrorCode task that cost $2,600 to run
Epoch AI's MirrorCode benchmark reveals Claude Opus 4.7 as the leader with a 56% solve rate, reconstructing a 16,000-line toolkit in 14 hours. Despite this, all models tested struggle with the most complex tasks, highlighting limitations in current AI capabilities. The single task consumed $2,600 over 19 days, raising questions about cost-effectiveness in AI development.
