Instructions to use AbelWa/Test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AbelWa/Test with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AbelWa/Test",
	filename="Audar-ASR-V1-Turbo-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "\"sample1.flac\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AbelWa/Test with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AbelWa/Test:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AbelWa/Test:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AbelWa/Test:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AbelWa/Test:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf AbelWa/Test:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AbelWa/Test:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf AbelWa/Test:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AbelWa/Test:Q4_K_M

Use Docker

docker model run hf.co/AbelWa/Test:Q4_K_M

LM Studio
Jan
Ollama
How to use AbelWa/Test with Ollama:
```
ollama run hf.co/AbelWa/Test:Q4_K_M
```

Unsloth Studio

How to use AbelWa/Test with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AbelWa/Test to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AbelWa/Test to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AbelWa/Test to start chatting

Atomic Chat new
Docker Model Runner
How to use AbelWa/Test with Docker Model Runner:
```
docker model run hf.co/AbelWa/Test:Q4_K_M
```

Lemonade

How to use AbelWa/Test with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AbelWa/Test:Q4_K_M

Run and chat with the model

lemonade run user.Test-Q4_K_M

List all available models

lemonade list

Test / README.md

AbelWa

Upload folder using huggingface_hub

8406653 verified about 16 hours ago

preview code

Raw

History Blame Contribute Delete

10.9 kB

metadata

license: other
license_name: audarai-community-license-v1.0
license_link: https://www.audarai.com/license/audarai-community-license-v1.0/
language:
  - ar
  - en
pipeline_tag: automatic-speech-recognition
inference: false
tags:
  - automatic-speech-recognition
  - asr
  - speech-recognition
  - arabic
  - arabic-asr
  - dialectal-arabic
  - emirati
  - gulf-arabic
  - streaming
  - realtime
  - gguf
  - llama-cpp
  - audar

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

🧭 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

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 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 %.

System	CommonVoice-18	MASC-clean	MASC-noisy	MGB-2	SADA	Casablanca	Avg
Audar-ASR-V1-Turbo	3.55	9.13	16.84	14.01	35.22	62.87	23.60
ElevenLabs Scribe v1	5.74	9.87	19.78	15.15	40.87	66.93	26.39
Qwen3-ASR-1.7B (base)	10.86	15.07	21.12	29.21	50.54	85.25	35.34
Whisper-Large-v3	17.83	24.66	34.63	16.26	55.96	71.81	36.86

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.

Measured on an internal dialectal validation sample

Same sample and harness as the Flash card — useful for a direct Flash-vs-Turbo comparison (WER/CER %, N clips per set).

Set (dialect)	N	WER %	CER %
SawtArabi (Gulf)	23	13.7	2.7
ArzEn (Egyptian ⇄ English code-switch)	40	19.9	9.2
MGB-3 (Egyptian broadcast)	40	27.3	10.5
Casablanca (Maghrebi / Moroccan Darija)	40	61.9	28.6

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

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}
}

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