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What it is

lemura-arabic-asr-lite is a compact multi-dialect Arabic ASR model built for accuracy and efficiency. It is a FastConformer-CTC acoustic model adapted in-house from the NVIDIA FastConformer foundation — the contribution is the adaptation (the Arabic data curriculum and dialect coverage), not the foundation.

• Small and fast — ~115M parameters; runs comfortably on CPU and in real time, no GPU required.
• Dialect-aware — fine-tuned on ~2,900 hours of Arabic spanning MSA and the Gulf, Egyptian, Levantine and Maghrebi dialect groups, not MSA-only.
• Robust on real audio — strongest on broadcast, conversational and Gulf/MSA speech.
• Open and simple — a single .nemo file, loadable in a few lines with NVIDIA NeMo.

The result is leaderboard-grade Arabic ASR at edge scale — #2 of 36 systems on the Open Universal Arabic ASR Leaderboard, ahead of every 2B–30B audio-LLM evaluated, at ~115M parameters.

Model summary

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, lemura-arabic-asr-lite ranks #2 of 36 systems with an average WER of 25.08 % — ahead of every model evaluated except one, including systems 17× to 250× larger.

Open Universal Arabic ASR Leaderboard — full standings

Per-dataset WER % across all six leaderboard test sets. Lower is better; Avg WER is the ranking metric. Our rows were produced with the official leaderboard code (same normalizer, same WER metric). Ours in bold.

Bold = our model and our best-in-column results. lemura-arabic-asr-lite is the single best system on MASC-clean (7.27) and MASC-noisy (23.65) of every model evaluated, and the highest-ranked system below 1B parameters by a wide margin. Casablanca (Moroccan Darija) is the hardest set for every system.

Competitor rows are the published Open Universal Arabic ASR Leaderboard standings, reproduced for context; they are not our measurements.

Efficiency

The systems around us on the leaderboard are large generative audio-LLMs (2–30B parameters) that need GPUs. lemura-arabic-asr-lite reaches the same accuracy tier with a ~115M-parameter CTC model:

That makes it practical for on-device, low-cost and high-throughput Arabic transcription where the larger models are impractical.

Inference

import nemo.collections.asr as nemo_asr

model = nemo_asr.models.ASRModel.restore_from("asr_final.nemo")
print(model.transcribe(["audio.wav"]))   # 16 kHz mono

Try it live, no install: lemura-arabic-asr demo

Languages, dialects and tasks

Intended use and limitations

Intended: transcribing Arabic speech across dialects — call centres, voice notes, media captioning, voice interfaces, and any deployment where CPU-only or real-time operation matters.

Limitations:

• Maghrebi/Darija (Casablanca, 58.24 WER) remains hard, as it does for every system on the leaderboard.
• Very noisy or far-field audio degrades accuracy.
• CTC output has no built-in punctuation or diacritic restoration.
• May reflect biases present in the training corpora.

Related model

For a larger, generative alternative with broader dialect fine-tuning, see lemura-arabic-asr-qwen3 (1.7B, audio-LLM). Note its published WER is an in-domain figure and is not directly comparable to the zero-shot leaderboard numbers above.

License

CC-BY-4.0.

Citation

@misc{lemura_arabic_asr_lite_2026,
  title  = {lemura-arabic-asr-lite: Compact Multi-Dialect Arabic Speech Recognition},
  author = {Lemura Labs},
  year   = {2026},
  url    = {https://huggingface.co/lemuralabs/lemura-arabic-asr-lite}
}

About Lemura Labs

Arabic-first, efficiency-first speech intelligence.

Lemura Labs builds compact, deployable speech and language models — accuracy at a size and cost that works outside the datacentre.