Audar-ASR-Flash

🧭 What it is

Audar-ASR-V1-Flash is the edge tier of Audar's Arabic-first 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 built on a permissively-licensed open-weight audio-LLM foundation, then adapted in-house through Audar's Arabic training program — the contribution is the adaptation, not the foundation:

• 🧱 Large-scale bilingual pretraining — 300,000+ hours of labeled audio, primarily Arabic and English (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.

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. Runs via Transformers, llama.cpp / GGUF, and vLLM.

Model summary

📊 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 (Audar rows below are the leaderboard maintainers' independent reproduction, Aug 2026 normalization). Audar-ASR-V1-Flash scores 32.0 avg WER at just 0.78B parameters — on par with Qwen3-ASR-1.7B (2× its size) and ahead of Voxtral-Small-24B, Whisper-large-v3, and every CTC baseline. 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.