---
title: "Arabic"
description: "2 posts about Arabic, summarised, each linking to the original."
---

# Arabic
> 2 posts about Arabic, summarised, each linking to the original.

## Articles

### [Introducing Falcon ASR](https://yomu.fyi/post/introducing-falcon-asr.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Abdul Muneer, Rishabh Saraf, Ramesh Gundluru, Shamsa Hamad, Lepauloux, Hakim Hacid
- Published: Oct 7, 2026
- Type: Announcement

The Technology Innovation Institute in Abu Dhabi released Falcon-ASR, a 1.6 billion parameter speech recognition model focused on Arabic, especially the Emirati dialect, while also supporting English, French, Spanish, and Portuguese. On the Open Universal Arabic ASR Leaderboard, Falcon-ASR achieved a 20.92% average word error rate across six test sets, outperforming the best published average of 23.17% in the reference snapshot. The model recorded a 22.73% word error rate and 10.19% character error rate on an internal Emirati evaluation, 4.07% better in word error rate than the next best system compared. Training data included background noise, overlapping speech, music, reverberation, telephony effects, speed, and pitch variations to handle diverse recording conditions. Falcon-ASR builds on the Falcon3-Audio architecture and provides word-level timestamps linking transcribed words to audio positions.


### [Falcon-Emirati: When an LLM Learns the Dialect, the Culture, and the Nuance](https://yomu.fyi/post/falcon-emirati-when-an-llm-learns-the-dialect-the-culture-and-the-nuan.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Shaikha Alsuwaidi, Omar saif alkaabi, Maitha Alhammadi, Ahmed Alzubaidi, Mohammed Alyafeai, Leen AlQadi, Basma Boussaha, Hakim Hacid
- Published: Oct 6, 2026
- Type: Announcement

Falcon-Emirati-7B is a dialect-specialized language model built on Falcon-H1-Arabic to understand and generate Emirati Arabic, a Gulf dialect with distinct vocabulary and cultural context that Modern Standard Arabic models miss. The model uses a hybrid architecture combining Mamba state-space models and Transformer attention running in parallel inside every block, with fused outputs for efficient long-context processing. Training required a dedicated data pipeline of authentic Emirati web content, MSA material about Emirati culture, and synthetic data guided by strict glossaries and style rules to cover gaps in available dialectal text. On the Alyah benchmark, Falcon-Emirati-7B scored 84.83%, outperforming larger multilingual models, and achieved 85.57% on the UAE cultural understanding task in ArabCulture-Dialogue. The 7B parameter size was chosen as the best balance of quality against training and inference cost for dialect adaptation.