Audar-ASR-V1-Turbo · GGUF

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

From Arabic to the world.

🧭 Overview · 📊 Benchmarks · 💻 GGUF Deploy · 🎙️ Streaming · ☁️ Audar API · 📜 License

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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 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.

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

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

📊 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 35 systems with the lowest average WER (24.8 %) and the lowest average CER (9.5 %) of any model evaluated — ahead of the next-best system by 1.1 pp WER — and it is the single best system on SADA and MGB-2.

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. Numbers are our end-to-end re-run on the full test sets with the leaderboard-equivalent normalizer — calibrated to the public leaderboard within 0.03 pp (bit-exact on 4 of 6 sets for Qwen3-ASR-1.7B), so every row is directly comparable. Ours in bold.

#	Model	Avg WER	Avg CER	SADA	CV-18	MASC-clean	MASC-noisy	MGB-2	Casablanca
1	Audar-ASR-V1-Turbo (Ours)	24.78	9.49	29.41	8.60	19.60	28.35	11.13	51.58
2	CohereLabs/cohere-transcribe-arabic-07-2026	25.87	11.80	37.47	5.82	19.60	27.07	15.54	49.71
3	omnilingual-asr/omniASR_LLM_7B	28.32	12.52	41.61	8.75	19.69	29.29	14.13	56.46
4	omnilingual-asr/omniASR_LLM_3B	29.96	13.77	46.18	9.15	19.90	30.03	14.22	60.27
5	omnilingual-asr/omniASR_LLM_1B	29.96	13.40	43.84	9.55	20.03	30.26	15.34	60.68
6	CohereLabs/cohere-transcribe-03-2026	30.67	16.37	60.11	8.17	8.66	19.01	25.33	62.71
7	Qwen/Qwen3-Omni-30B-A3B-Instruct	30.71	13.67	44.82	11.46	21.47	30.85	13.09	62.55
8	nvidia-conformer-ctc-large-arabic (lm)	32.91	13.84	44.52	8.80	23.74	34.29	17.20	68.90
9	omnilingual-asr/omniASR_LLM_300M	32.96	14.84	51.38	12.03	20.66	32.45	16.58	64.64
10	google/gemma-4-E4B-it	32.98	13.71	43.40	19.65	24.86	33.59	17.72	58.63
11	Qwen/Qwen3-ASR-1.7B	33.36	12.33	45.53	16.90	24.37	34.29	16.57	64.47
12	mistralai/Voxtral-Small-24B-2507	34.47	15.29	50.82	15.25	23.96	34.43	16.03	66.30
13	nvidia-conformer-ctc-large-arabic (greedy)	34.74	13.37	47.26	10.60	24.12	35.64	19.69	71.13
14	google/gemma-4-E2B-it	35.87	15.34	46.23	23.76	27.47	36.15	20.72	60.87
15	openai/whisper-large-v3	36.86	17.21	55.96	17.83	24.66	34.63	16.26	71.81
16	omnilingual-asr/omniASR_CTC_3B	37.78	19.79	69.85	14.19	21.48	34.60	18.96	67.58
17	omnilingual-asr/omniASR_CTC_7B	38.12	20.91	72.69	12.47	21.08	35.04	20.43	67.02
18	facebook/seamless-m4t-v2-large	38.16	17.03	62.52	21.70	25.04	33.24	20.23	66.25
19	omnilingual-asr/omniASR_CTC_1B	39.29	20.47	71.42	17.55	22.76	35.73	19.96	68.32
20	openai/whisper-large-v3-turbo	40.05	18.87	60.36	25.73	25.51	37.16	17.75	73.79
21	openai/whisper-large-v2	40.20	19.55	57.46	21.77	27.25	38.55	25.17	71.01
22	Qwen/Qwen3-ASR-0.6B	42.19	16.23	53.75	28.28	31.34	42.63	25.45	71.68
23	openai/whisper-large	42.57	20.49	63.24	26.04	28.89	40.79	24.28	72.18
24	mistralai/Voxtral-Mini-3B-2507	42.58	19.90	63.65	22.12	28.37	41.27	22.56	77.52
25	asafaya/hubert-large-arabic-transcribe	45.50	17.35	67.82	8.01	32.94	50.16	37.51	76.53
26	openai/whisper-medium	45.57	22.27	67.71	28.07	29.99	42.91	29.32	75.44
27	nvidia-Parakeet-ctc-1.1b-concat	46.54	23.88	70.70	26.34	30.49	45.95	24.94	80.80
28	omnilingual-asr/omniASR_CTC_300M	46.65	21.86	78.11	27.90	28.40	43.26	26.85	75.35
29	nvidia-Parakeet-ctc-1.1b-universal	51.96	25.19	73.58	40.01	36.16	50.03	30.68	81.30
30	microsoft/VibeVoice-ASR	52.99	28.95	69.83	44.25	32.95	52.43	25.10	93.37
31	facebook/mms-1b-all	54.54	21.45	77.48	26.52	38.82	57.33	39.16	87.95
32	openai/whisper-small	55.13	21.68	78.02	24.18	35.93	56.36	48.64	87.64
33	whitefox123/w2v-bert-2.0-arabic-4	58.13	27.62	87.34	41.79	37.82	53.28	40.66	87.88
34	jonatasgrosman/wav2vec2-large-xlsr-53-arabic	60.98	25.61	86.82	23.00	42.75	64.27	56.29	92.72
35	speechbrain/asr-wav2vec2-commonvoice-14-ar	65.74	30.93	88.54	29.17	49.10	69.57	64.37	93.68

Bold = best in column. Audar-ASR-V1-Turbo owns both composite averages and leads on SADA and MGB-2; the recent Cohere and OmniASR systems are the closest competitors, each strongest on a subset of the conversational and clean-read sets. Casablanca (Moroccan Darija) is the hardest set for every system.

Emirati Arabic

Set	WER %	CER %
Emirati (Mixat, full 1,585-clip test)	19.4	7.3

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.

💻 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

File	Approx. size	Notes
Audar-ASR-V1-Turbo-Q4_K_M.gguf	~1.28 GB	Smallest; constrained hardware
Audar-ASR-V1-Turbo-Q8_0.gguf	~2.16 GB	Near-lossless (recommended)
Audar-ASR-V1-Turbo.gguf (BF16)	~4.07 GB	Full precision decoder
mmproj-Audar-ASR-V1-Turbo.gguf	~0.64 GB	BF16 audio encoder — required, keep BF16

🎙️ 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}
}

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About AudarAI

Leading Arabic-First Multilingual Audio Intelligence

AudarAI starts with Arabic — and expands to the world.

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.

🌐 www.audarai.com · 🤗 Hugging Face · GitHub · contact@audarai.com

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