Audar-ASR

Audar's Arabic-first speech-recognition model — leaderboard-grade, dialect-aware.

From Arabic to the world.

🧭 What it is

Audar-ASR-V1-Turbo is an Arabic-first generative speech-recognition model — the accuracy tier of the Audar-ASR family. It recasts transcription as audio-conditioned next-token prediction over a unified text vocabulary (a language-model decoder rather than a CTC or transducer objective), and is built on a permissively-licensed open-weight audio-LLM foundation and adapted in-house — the contribution is the adaptation (the data curriculum and the alignment rubric), not the foundation:

• 🧱 Large-scale bilingual pretraining — 300,000+ hours of labeled audio, primarily Arabic and English, spanning MSA, Gulf, Egyptian, Levantine and Maghrebi speech, code-switching, and diverse acoustic channels.
• 🎯 Dialect-targeted fine-tuning — hardness sampling and multi-task conditioning focused on proper nouns, code-switching, and dialect-faithful orthography.
• 🧠 KTO preference alignment — Kahneman-Tversky Optimization on accented dialectal Arabic, with unpaired binary-desirability labels from trained native annotators across the Gulf, Levantine, Egyptian, and Maghrebi dialects, along five axes: verbatim accuracy, diacritic correctness, code-switch handling, named-entity preservation, and output formatting.

The result is state-of-the-art dialectal Arabic ASR — the lowest average WER and CER of any evaluated system on the Open Universal Arabic ASR Leaderboard. It transcribes MSA and every major Arabic dialect, code-switched Arabic–English, and English, across 30 languages in total.

Built on a permissively-licensed open-weight audio-LLM foundation; the adaptation, data, and alignment are Audar's. Full method and results: Audar-ASR-V1 Technical Report.

Model summary

📊 Benchmarks

Arabic dialectal ASR is hard — heavily dialectal, conversational, code-switched speech is the frontier for every system. On the Open Universal Arabic ASR Leaderboard, Audar-ASR-V1-Turbo ranks #1 of 37 systems with the lowest average WER (23.2 %) and the lowest average CER (9.2 %) of any model evaluated — and it is the single best system on SADA, MASC-clean, MGB-2 and Casablanca.

Open Universal Arabic ASR Leaderboard — full standings

Per-dataset WER % across all six leaderboard test sets, plus the two composite averages. Lower is better; Avg WER is the ranking metric. Audar rows show the leaderboard maintainers' independent reproduction (Aug 2026) under the leaderboard's current normalization; other rows are as previously published by the leaderboard and may shift slightly when the full board is recomputed under the updated normalization. Ours in bold.