Audar-ASR

About

What Audar-ASR is

Audar-ASR-V1-Turbo · GGUF Audar's Arabic-first speech-recognition model — leaderboard-grade, dialect-aware. From Arabic to the world. [](https://github.com/AudarAI/Audar-ASR-V1) 🧭 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 sin

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How audio & music work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys audar-asr for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (audar-asr below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/audio/transcriptions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -F model="audar-asr" -F file=@audio.mp3

Details

Languages, data & research

Licence

Commercial licence needed

Sources

Weights & code

Explore

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