AbelWa/Test

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Audar-ASR-V1-Turbo · GGUF

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

From Arabic to the world.

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<p><a href="#-what-it-is"><b>🧭 Overview</b></a> · <a href="#-benchmarks"><b>📊 Benchmarks</b></a> · <a href="#-gguf-inference-llamacpp"><b>💻 GGUF Deploy</b></a> · <a href="#-real-time-streaming"><b>🎙️ Streaming</b></a> · <a href="https://www.audarai.com"><b>☁️ Audar API</b></a> · <a href="https://www.audarai.com/license/audarai-community-license-v1.0/"><b>📜 License</b></a></p>

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🧭 What it is

Audar-ASR-V1-Turbo is Audar's proprietary Arabic 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 developed in-house through a proprietary Arabic training program:

• —🧱 Large-scale dialectal pretraining — 300,000+ hours of Arabic audio 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.
• —🧠 GRPO reinforcement-learning alignment — preference optimization against Arabic-native failure modes (diacritization, code-switching, named-entity preservation, formatting) with trained native annotators.

The result is state-of-the-art dialectal Arabic ASR — the lowest average WER 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. For real-time, edge, or high-throughput deployment, see the smaller **Audar-ASR-V1-Flash**.

Distributed in the widely-supported Qwen3-ASR architecture format for turnkey tooling (llama.cpp / GGUF). The model — data, training curriculum, and alignment — is Audar's.

Model summary

<table> <tbody> <tr><td width="200"><b>Model</b></td><td>Audar-ASR-V1-Turbo — proprietary Arabic ASR (accuracy tier)</td></tr> <tr><td><b>Task</b></td><td>Automatic speech recognition (audio → text)</td></tr> <tr><td><b>Approach</b></td><td>Generative ASR — audio encoder + language-model decoder (audio-conditioned next-token prediction)</td></tr> <tr><td><b>Training</b></td><td>300k+ hrs dialectal pretraining → dialect-targeted SFT → GRPO alignment</td></tr> <tr><td><b>Decoder parameters</b></td><td>2,031,739,904 (2.03B)</td></tr> <tr><td><b>Audio encoder parameters</b></td><td>317,477,504 (0.32B)</td></tr> <tr><td><b>Total parameters</b></td><td>2,349,217,408 (2.35B, bf16)</td></tr> <tr><td><b>Audio input</b></td><td>16 kHz mono; 30 s context (longer audio is chunked/streamed)</td></tr> <tr><td><b>Languages</b></td><td>Arabic (MSA + Gulf/Egyptian/Levantine/Maghrebi dialects) + English + 28 more</td></tr> <tr><td><b>Runtime</b></td><td>GGUF / llama.cpp — CPU · GPU · edge</td></tr> <tr><td><b>License</b></td><td>AudarAI Community License v1.0</td></tr> </tbody> </table>

📊 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 posts the lowest average WER of any evaluated system on the full test sets — 24.7 %, best on four of the six — and 3.55 % WER on CommonVoice-18 Arabic. The per-dataset development-protocol results (100 utterances/benchmark) are below.

Open Universal Arabic ASR Leaderboard — WER % (lower is better)

Per-dataset WER (%), development protocol (100 utterances/benchmark); baselines are the leaderboard's published full-test scores. Best per column in bold. Authoritative full-test-set average: 24.7 %.

Emirati Arabic

On Emirati, the real recognition error is ≈ 7.3 % — near-parity with spontaneous English — while the residual up to 19.4 % WER is largely orthographic convention (near-miss spelling of the same word, e.g. انتو↔انتوا, and Latin-vs-Arabic rendering of English loanwords), not misrecognition.

Measured on an internal dialectal validation sample

Same sample and harness as the [Flash card](https://huggingface.co/audarai/Audar-ASR-V1-Flash#-benchmarks) — useful for a direct Flash-vs-Turbo comparison (WER/CER %, N clips per set).

Casablanca 61.9 WER ≈ the official leaderboard's 62.87 (reproduced in-house) — the numbers line up.

💻 GGUF inference (llama.cpp)

Turbo runs on llama.cpp via the multimodal (mtmd) path — a quantized decoder GGUF plus a BF16 audio projector (mmproj). Build a recent llama.cpp (with Qwen3-ASR support), then:

./llama-mtmd-cli \
  -m       Audar-ASR-V1-Turbo-Q8_0.gguf \
  --mmproj mmproj-Audar-ASR-V1-Turbo.gguf \
  --audio  clip.wav \
  -sys     "فرّغ الكلام العربي التالي." \
  --temp 0

⚠️ The audio projector (`mmproj`) must stay BF16 (its ClippableLinear is numerically sensitive). The decoder quantizes normally.

Prefer a managed endpoint? The Audar-ASR family is also available via the **Audar API/SDK** — streaming, speaker-attributed transcription, and diarization, production-hosted.

GGUF variants

🎙️ Real-time streaming

Audar-ASR streams via LocalAgreement-2: as audio arrives the trailing window is re-decoded each hop and a word is committed only once two consecutive decodes agree on it — giving stable, low-latency incremental output over the GGUF runtime. Audar's production realtime engine serves the same policy over an OpenAI-Realtime-compatible WebSocket with model-based endpointing and ≥64 concurrent streams on a single A100-80GB.

🌍 Languages, dialects & tasks

• —Primary: Arabic — MSA and dialectal (Gulf/Emirati, Egyptian, Levantine, Maghrebi), plus code-switched Arabic–English; emits dialect-faithful orthography from audio alone.
• —Also: English + 28 additional languages.
• —Task: transcription (audio → UTF-8 text), prompt-steerable for language and formatting.

Intended use & limitations

Intended use. Broadcast/media transcription, meeting & contact-center intelligence, voice agents, captioning, and accessibility — cloud or on-prem.

Limitations.

• —Maghrebi / Moroccan Darija (Casablanca) remains the hardest condition (~63 % WER) for all systems.
• —Heavily code-switched telephony and low-SNR audio degrade accuracy relative to clean MSA.
• —Long-form audio can drift on very long recordings.
• —Not evaluated for, and must not be used for, covert speaker identification.

📜 License

Released under the AudarAI Community License v1.0 — research and limited commercial use for qualifying Community Entities; enterprise / large-scale / MaaS use requires an AudarAI Enterprise License. See audarai.com/license/audarai-community-license-v1.0.

Citation

@misc{audar-asr-turbo-2026,
  title  = {Audar-ASR: Dialect-Aware Arabic Speech Recognition},
  author = {AudarAI},
  year   = {2026},
  note   = {Audar-ASR-V1-Turbo},
  url    = {https://huggingface.co/audarai/Audar-ASR-V1-Turbo}
}

About AudarAI

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Leading Arabic-First Multilingual Audio Intelligence

AudarAI starts with Arabic — and expands to the world.

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We are building advanced multilingual audio intelligence that helps individuals, enterprises, and governments communicate across languages, cultures, and borders. By combining Arabic-first speech technology with global multilingual AI, AudarAI transforms voice into understanding, interaction, and connection.

Our work spans speech recognition, speech understanding, voice-enabled digital assistants, human-computer interaction, and intelligent audio systems designed for real-world impact. From empowering people to access technology in their native language to helping organizations communicate globally, AudarAI is shaping a future where every voice can be heard, understood, and connected.

Arabic-first. Multilingual by design. Human-centered at heart.

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[🌐 www.audarai.com](https://www.audarai.com) · 🤗 Hugging Face · GitHub · contact@audarai.com

© 2026 AUDARAI PTE. LTD. · Licensed under the AudarAI Community License v1.0

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