Today, we are releasing Cohere Transcribe Arabic as an open-source model. Based on our 2B frontier Automatic Speech Recognition (ASR) model released earlier this year, Cohere Transcribe Arabic is built for the realities of Arabic in business and developer settings: dialect variation, bilingual Arabic-English speech, code-switching, and domain-specific vocabulary.It is the most accurate, open-source Arabic speech-to-text model to date, outperforming leading alternatives, including Whisper and OmniASR, across dialects and common speech patterns. It also delivers substantial gains over Cohere Transcribe on both Arabic and bilingual Arabic-English audio.Cohere Transcribe Arabic is available under the Apache 2.0 license. Developers can download the weights and read our quickstart implementations on Hugging Face, or access the hosted model through the Cohere API or Model Vault.One model, many voicesArabic is a remarkably rich language in both script and speech. More than 300 million people speak Arabic as their mother tongue, across roughly 30 recognized varieties shaped by distinct cultural, regional, and historical contexts. Saudi Arabia alone is home to three major dialect groups and many more linguistic subgroups.This diversity, however, heavily complicates efforts towards normalisation. While Modern Standard Arabic (MSA) provides a de facto common written standard, everyday speech varies significantly across dialects. Morphological differences, regional pronunciation, and code-switching — the use of Arabic and non-Arabic vocabulary in the same conversation, often in professional settings — make a single, uniform approach to communication difficult.Image 1: a map of the Arabic-speaking world, highlighting some of the largest dialect families, their number of native speakers, and geographic distribution. Source: EthnologueThe challenge is especially stark in the development of natural language technology, such as ASR. How do you train a model that preserves dialectal nuance while remaining useful beyond a particular market? The result has been a frontier-language gap: Arabic remains under-served by state-of-the-art AI systems while English continues to dominate model development and evaluation.To help narrow that gap, Cohere embarked on a simple mission: build an enterprise-ready solution that lets Arabic users speak in their natural voice.We started with Cohere Transcribe (launched in March with leading English-language accuracy and broad multilingual coverage) and trained it extensively on data spanning Arabic dialects, professional language, code-switching, and varied acoustic conditions.The result is a new state-of-the-art solution in how Arabic speech is captured, ready for production use and openly available to all.Unrivalled accuracyCohere 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.