license: other
license_name: audarai-open-license-v1.0
license_link: https://www.audarai.com/license/audarai-open-license-v1.0/
language:
- ar
- en
pipeline_tag: automatic-speech-recognition
library_name: transformers
inference: false
tags:
- automatic-speech-recognition
- asr
- speech-recognition
- arabic
- arabic-asr
- dialectal-arabic
- emirati
- gulf-arabic
- streaming
- realtime
- gguf
- llama-cpp
- on-device
- edge
- audar
Audar-ASR-V1-Flash · Transformers + GGUF
Audar's proprietary Arabic ASR — the real-time, edge tier.
From Arabic to the world.
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🧭 Overview · 📊 Benchmarks · 🤗 Transformers · 💻 GGUF · 🎙️ Streaming · 🐙 GitHub · ☁️ Audar API · 📜 License
🧭 What it is
Audar-ASR-V1-Flash is the edge tier of Audar's proprietary Arabic speech-recognition family — the same in-house Arabic training program as Audar-ASR-V1-Turbo, delivered in a fast ~0.6B-decoder model for real-time captioning and on-device use. It recasts transcription as audio-conditioned next-token prediction (a language-model decoder, not CTC/transducer), and is developed through Audar's proprietary pipeline:
- 🧱 Large-scale dialectal pretraining — 300,000+ hours of Arabic audio (MSA + Gulf, Egyptian, Levantine, Maghrebi; code-switching; diverse channels).
- 🎯 Dialect-targeted fine-tuning with hardness and multi-task sampling.
- 🧠 KTO preference alignment (Kahneman-Tversky Optimization) from trained native-Arabic annotators.
It transcribes MSA and every major Arabic dialect, code-switched Arabic–English, and English, across 30 languages, and runs on CPU / GPU / edge via 🤗 Transformers or GGUF. For maximum accuracy on the hardest dialectal audio, use the larger Turbo tier.
Distributed in the widely-supported Qwen3-ASR architecture format for turnkey tooling (Transformers, llama.cpp / GGUF). The model — data, training curriculum, and alignment — is Audar's.
Model summary
| Model | Audar-ASR-V1-Flash — proprietary Arabic ASR (edge tier) |
| Task | Automatic speech recognition (audio → text) |
| Approach | Generative ASR — audio encoder + language-model decoder |
| Training | 300k+ hrs dialectal pretraining → dialect-targeted SFT → KTO preference alignment |
| Decoder parameters | 596,049,920 (0.60B) |
| Audio encoder parameters | 186,376,192 (0.19B) |
| Total parameters | 782,426,112 (0.78B, 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 |
| Runtimes | 🤗 Transformers (GPU) · GGUF / llama.cpp (CPU · GPU · edge) |
| License | AudarAI Open License v1.0 |
📊 Benchmarks
Open Universal Arabic ASR Leaderboard — full standings
Flash is evaluated end-to-end on all six leaderboard test sets (full test splits, not sampled), with the leaderboard-equivalent normalizer — the same harness and protocol as every other row (calibrated to the public leaderboard within 0.03 pp). Audar-ASR-V1-Flash ranks #11 of 36 systems at just 0.78B parameters — the strongest small model on the board: it beats Qwen3-ASR-1.7B (2× its size), Voxtral-Small-24B, Whisper-large-v3, and every CTC baseline, trailing only 10 systems (several 3–30B). Audar's accuracy tier, Turbo, is #1.
Per-dataset WER % across all six sets, plus the two composite averages. Lower is better; Avg WER is the ranking metric. Flash and Turbo (Ours) in bold; bold cell = best in column.
| # | Model | Avg WER | Avg CER | SADA | CV-18 | MASC-clean | MASC-noisy | MGB-2 | Casablanca |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Audar-ASR-V1-Turbo (Ours, 2.35B) | 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 | Audar-ASR-V1-Flash (Ours, 0.78B) | 33.31 | 13.66 | 44.53 | 16.02 | 25.96 | 35.43 | 17.11 | 60.79 |
| 12 | Qwen/Qwen3-ASR-1.7B | 33.36 | 12.33 | 45.53 | 16.90 | 24.37 | 34.29 | 16.57 | 64.47 |
| 13 | mistralai/Voxtral-Small-24B-2507 | 34.47 | 15.29 | 50.82 | 15.25 | 23.96 | 34.43 | 16.03 | 66.30 |
| 14 | nvidia-conformer-ctc-large-arabic (greedy) | 34.74 | 13.37 | 47.26 | 10.60 | 24.12 | 35.64 | 19.69 | 71.13 |
| 15 | google/gemma-4-E2B-it | 35.87 | 15.34 | 46.23 | 23.76 | 27.47 | 36.15 | 20.72 | 60.87 |
| 16 | openai/whisper-large-v3 | 36.86 | 17.21 | 55.96 | 17.83 | 24.66 | 34.63 | 16.26 | 71.81 |
| 17 | omnilingual-asr/omniASR_CTC_3B | 37.78 | 19.79 | 69.85 | 14.19 | 21.48 | 34.60 | 18.96 | 67.58 |
| 18 | omnilingual-asr/omniASR_CTC_7B | 38.12 | 20.91 | 72.69 | 12.47 | 21.08 | 35.04 | 20.43 | 67.02 |
| 19 | facebook/seamless-m4t-v2-large | 38.16 | 17.03 | 62.52 | 21.70 | 25.04 | 33.24 | 20.23 | 66.25 |
| 20 | omnilingual-asr/omniASR_CTC_1B | 39.29 | 20.47 | 71.42 | 17.55 | 22.76 | 35.73 | 19.96 | 68.32 |
| 21 | openai/whisper-large-v3-turbo | 40.05 | 18.87 | 60.36 | 25.73 | 25.51 | 37.16 | 17.75 | 73.79 |
| 22 | openai/whisper-large-v2 | 40.20 | 19.55 | 57.46 | 21.77 | 27.25 | 38.55 | 25.17 | 71.01 |
| 23 | Qwen/Qwen3-ASR-0.6B | 42.19 | 16.23 | 53.75 | 28.28 | 31.34 | 42.63 | 25.45 | 71.68 |
| 24 | openai/whisper-large | 42.57 | 20.49 | 63.24 | 26.04 | 28.89 | 40.79 | 24.28 | 72.18 |
| 25 | mistralai/Voxtral-Mini-3B-2507 | 42.58 | 19.90 | 63.65 | 22.12 | 28.37 | 41.27 | 22.56 | 77.52 |
| 26 | asafaya/hubert-large-arabic-transcribe | 45.50 | 17.35 | 67.82 | 8.01 | 32.94 | 50.16 | 37.51 | 76.53 |
| 27 | openai/whisper-medium | 45.57 | 22.27 | 67.71 | 28.07 | 29.99 | 42.91 | 29.32 | 75.44 |
| 28 | nvidia-Parakeet-ctc-1.1b-concat | 46.54 | 23.88 | 70.70 | 26.34 | 30.49 | 45.95 | 24.94 | 80.80 |
| 29 | omnilingual-asr/omniASR_CTC_300M | 46.65 | 21.86 | 78.11 | 27.90 | 28.40 | 43.26 | 26.85 | 75.35 |
| 30 | nvidia-Parakeet-ctc-1.1b-universal | 51.96 | 25.19 | 73.58 | 40.01 | 36.16 | 50.03 | 30.68 | 81.30 |
| 31 | microsoft/VibeVoice-ASR | 52.99 | 28.95 | 69.83 | 44.25 | 32.95 | 52.43 | 25.10 | 93.37 |
| 32 | facebook/mms-1b-all | 54.54 | 21.45 | 77.48 | 26.52 | 38.82 | 57.33 | 39.16 | 87.95 |
| 33 | openai/whisper-small | 55.13 | 21.68 | 78.02 | 24.18 | 35.93 | 56.36 | 48.64 | 87.64 |
| 34 | whitefox123/w2v-bert-2.0-arabic-4 | 58.13 | 27.62 | 87.34 | 41.79 | 37.82 | 53.28 | 40.66 | 87.88 |
| 35 | jonatasgrosman/wav2vec2-large-xlsr-53-arabic | 60.98 | 25.61 | 86.82 | 23.00 | 42.75 | 64.27 | 56.29 | 92.72 |
| 36 | speechbrain/asr-wav2vec2-commonvoice-14-ar | 65.74 | 30.93 | 88.54 | 29.17 | 49.10 | 69.57 | 64.37 | 93.68 |
Flash — per-dataset detail (full test sets)
Both metrics, for the six leaderboard sets and the composite average.
| Dataset | WER % | CER % |
|---|---|---|
| SADA | 44.53 | 23.63 |
| CommonVoice-18 | 16.02 | 5.04 |
| MASC-clean | 25.96 | 7.84 |
| MASC-noisy | 35.43 | 12.66 |
| MGB-2 | 17.11 | 7.97 |
| Casablanca | 60.79 | 24.85 |
| Average (6-set) | 33.31 | 13.66 |
Use Flash for real-time and on-device transcription; step up to Turbo when you need the lowest error on heavy dialectal or long-form audio — Turbo is #1 on the leaderboard (24.8 % avg WER) and cuts Flash's average WER by ~8.5 pp, with the biggest gains on SADA (44.5→29.4) and MGB-2 (17.1→11.1).
🤗 Transformers inference
Ships self-contained modeling code, so trust_remote_code=True is required.
# pip install "transformers>=4.57" torch librosa
import re, torch, librosa
from transformers import AutoProcessor, AutoModelForCausalLM
repo = "audarai/Audar-ASR-V1-Flash"
proc = AutoProcessor.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
repo, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="cuda:0",
).eval()
SYSTEM = "فرّغ الكلام العربي التالي." # "Transcribe the following Arabic speech."
audio, _ = librosa.load("clip.wav", sr=16000, mono=True)
conv = [
{"role": "system", "content": SYSTEM},
{"role": "user", "content": [{"type": "audio"}]}, # audio placeholder (a list, not "<audio>")
]
text = proc.apply_chat_template(conv, tokenize=False, add_generation_prompt=True)
inputs = proc(text=text, audio=audio, sampling_rate=16000, return_tensors="pt").to(model.device)
inputs["input_features"] = inputs["input_features"].to(model.dtype) # features are fp32 → cast to bf16
out = model.generate(**inputs, max_new_tokens=440, do_sample=False)
hyp = proc.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0]
print(re.sub(r"^\s*language\s+[A-Za-z]+\s*(?:<asr_text>)?\s*", "", hyp).strip())
- Language steering: the Arabic auto-dialect prompt above needs no dialect hint. For other
languages use e.g.
"Transcribe the following speech.". - Long audio (>30 s): split at ~30 s boundaries (see the streaming section).
💻 GGUF inference (llama.cpp)
Audar-ASR 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-Flash-Q8_0.gguf \
--mmproj mmproj-Audar-ASR-V1-Flash.gguf \
--audio clip.wav \
-sys "فرّغ الكلام العربي التالي." \
--temp 0
⚠️ The audio projector (
mmproj) must stay BF16 — the encoder'sClippableLinearis numerically sensitive, so F16/Q8 measurably degrade quality. The decoder quantizes normally.
GGUF variants
| File | Approx. size | Notes |
|---|---|---|
Audar-ASR-V1-Flash-Q4_K_M.gguf |
~0.40 GB | Smallest; best for edge/offline |
Audar-ASR-V1-Flash-Q8_0.gguf |
~0.64 GB | Near-lossless, CPU-friendly (recommended) |
Audar-ASR-V1-Flash.gguf (BF16) |
~1.20 GB | Full precision decoder |
mmproj-Audar-ASR-V1-Flash.gguf |
~0.38 GB | BF16 audio encoder — required, keep BF16 |
Prefer a managed endpoint? The Audar-ASR family is also available via the Audar API/SDK — streaming, speaker-attributed transcription, and diarization, production-hosted.
🎙️ Real-time streaming
The 30 s-context model 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 on both the Transformers and GGUF paths. Audar's production realtime engine serves the same policy over an OpenAI-Realtime-compatible WebSocket with model-based endpointing.
🌍 Languages, dialects & tasks
- Primary: Arabic — MSA and dialectal (Gulf/Emirati, Egyptian, Levantine, Maghrebi), plus code-switched Arabic–English; dialect-faithful orthography from audio alone.
- Also: English + 28 additional languages.
- Task: transcription (audio → UTF-8 text), prompt-steerable for language/formatting.
Intended use & limitations
Intended use. Live captioning and subtitles, voice assistants/agents, meeting and call-center transcription, media/broadcast, accessibility — cloud, on-prem, or offline/edge.
Limitations.
- Maghrebi / Moroccan Darija (Casablanca) is the hardest condition for all systems.
- Heavily code-switched telephony and low-SNR audio degrade accuracy relative to clean MSA.
- Long recordings can drift; chunk at sentence boundaries for best results.
- Not evaluated for, and must not be used for, covert speaker identification.
📜 License
Released under the AudarAI Open License v1.0 — commercial use, redistribution, and fine-tuning/quantization permitted; ship the license and keep notices. See audarai.com/license/audarai-open-license-v1.0.
Citation
@misc{audar-asr-flash-2026,
title = {Audar-ASR: Dialect-Aware Arabic Speech Recognition},
author = {AudarAI},
year = {2026},
note = {Audar-ASR-V1-Flash},
url = {https://huggingface.co/audarai/Audar-ASR-V1-Flash}
}
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 Open License v1.0