The Technology Innovation Institute (TII) in Abu Dhabi has released Falcon-ASR, a 1.6B parameter Arabic speech recognition model. It focuses on the Emirati dialect and also transcribes English, French, Spanish and Portuguese. In TII’s evaluation, Falcon-ASR posts a 20.92% average word error rate (WER) across 6 Arabic test sets. That beats the best published entry in TII’s leaderboard snapshot by 2.25 points. For developers building Arabic voice products, the catch is access: today it runs only as a hosted demo.
TL;DR
- Size: 1.6B parameters. Max audio context: Not disclosed (the demo accepts clips up to 60 seconds and 20 MB).
- Runs on: Not disclosed. No weights or quantized variants are published. Access is a Hugging Face demo, with API and native apps planned.
- Performance: Lowest Arabic and Emirati WER in TII’s own comparisons, with solid English results.
- Best: 20.92% average Arabic WER, 2.25 points below Audar-ASR-V1-Turbo (23.17%).
- Worst: 11.86% WER on Earnings-22, its weakest English test set.
- Bottom line (best): a compact 1.6B model leading larger Arabic systems in TII’s tests.
- Bottom line (worst): no weights, no license, no API yet, and all scores are self-reported.
What is Falcon-ASR?
Falcon-ASR is a speech-to-text model from TII that converts spoken Arabic and 4 other languages into text. All 5 languages share one set of weights. The model needs no language flag and outputs text in the language spoken. It also returns word-level timestamps, linking each word to its position in the audio. This is important for subtitling, searchable recordings and meeting notes.
How does Falcon-ASR handle Arabic dialects?
Dialectal Arabic has far fewer transcribed resources than Modern Standard Arabic (MSA). TII trained Falcon-ASR on Emirati, MSA, other Gulf and Arabic dialects, and English. The objective is everyday speech, including dialect forms and language switching.
Training audio was augmented with background noise, overlapping speech, music, room reverberation and telephony effects. Speed and pitch were varied too. The same treatment was applied to Emirati recordings.
The model builds on TII’s Falcon3-Audio work. That research describes audio-language models built on instruction-tuned LLMs and Whisper encoders, trained on under 30K hours of public audio. Our editorial and technical team at Marktechpost could not find any published Falcon-ASR’s exact architecture or training data size.
How does Falcon-ASR score on Arabic benchmarks?
TII followed the protocol of the Open Universal Arabic ASR Leaderboard, run by the ELM Research Center. It ranks systems by equal-weight average WER over 6 test sets. TII used the leaderboard’s pinned manifests. Competitor numbers are published leaderboard averages checked on 30 September 2026.
| Model | Params | Avg WER (%) | Avg CER (%) |
|---|---|---|---|
| Falcon-ASR | 1.6B | 20.92 | 8.79 |
| Audar-ASR-V1-Turbo | 2.35B | 23.17 | 9.23 |
| Cohere Transcribe Arabic (07-2026) | 2.0B | 25.87 | 11.80 |
| omniASR LLM 7B | 7.0B | 28.32 | 12.52 |
Lower is better. Note that Falcon-ASR’s figure is TII’s own run, not a leaderboard entry. The Audar model card lists its own average CER as 9.20%.
How does Falcon-ASR perform on Emirati speech?
TII built an internal Emirati and Gulf test set from held-out recordings with human-validated transcripts. It complements the UAE subset of the public Casablanca dataset.
| Model | Params | WER (%) | CER (%) |
|---|---|---|---|
| Falcon-ASR | 1.6B | 22.73 | 10.19 |
| Qwen3-Omni-30B-A3B-Instruct | 30B (3B active) | 26.80 | 12.72 |
| Audar-ASR-V1-Turbo | 2.35B | 27.89 | 13.75 |
| Cohere Transcribe Arabic | 2.0B | 31.05 | 18.07 |
| Qwen3-ASR-1.7B | 2.0B | 31.52 | 13.35 |
| Audar-ASR-V1-Flash | 0.78B | 32.87 | 15.36 |
Falcon-ASR leads by 4.07 WER points over Qwen3-Omni. This test set is private, so it cannot be reproduced. Audar reports 19.4% WER on a different Emirati set (Mixat), so the numbers are not directly comparable.
How good is Falcon-ASR at English?
On the 7 English test sets used by the Hugging Face Open ASR Leaderboard, TII reports a 5.74% mean WER. Per-set scores range from 1.75% on LibriSpeech clean to 11.86% on Earnings-22. For context, Qwen3-ASR-1.7B lists 5.76% mean WER on that leaderboard. French, Spanish and Portuguese are supported, but TII published no scores for them.
Comparison: Falcon-ASR vs closest Arabic ASR models
| Feature | Falcon-ASR | Audar-ASR-V1-Turbo | Cohere Transcribe Arabic | Qwen3-ASR-1.7B |
|---|---|---|---|---|
| Organization | TII (UAE) | AudarAI | Cohere | Alibaba Qwen |
| Parameters | 1.6B | 2.35B | 2B | 1.7B (2B on HF card) |
| Architecture | Builds on Falcon3-Audio | Audio encoder + LM decoder | FastConformer encoder + Transformer decoder | Built on Qwen3-Omni |
| License | Not disclosed | AudarAI Community License v1.0 | Apache 2.0 | Apache 2.0 |
| Open weights | No | Yes (BF16, GGUF, W4A16) | Yes | Yes |
| Languages | 5 (Arabic incl. Emirati, EN, FR, ES, PT) | 30 (Arabic-first) | Arabic, English | 30 languages + 22 Chinese dialects |
| Arabic avg WER (6 sets) | 20.92 (TII eval) | 23.17 | 25.87 | 33.36 (leaderboard) |
| Arabic avg CER | 8.79 (TII eval) | 9.20 | 11.80 | 12.33 |
| TII Emirati WER | 22.73 | 27.89 | 31.05 | 31.52 |
| English mean WER (Open ASR sets) | 5.74 (TII eval) | Not disclosed | Not disclosed | 5.76 |
| Timestamps | Word-level | Not disclosed | Not disclosed | Via Qwen3-ForcedAligner-0.6B |
| Runs on | Not disclosed (hosted demo) | CPU, GPU, edge via GGUF; W4A16 on 12 GB+ GPU | Not disclosed | GPU via Transformers or vLLM |
| Access | HF demo; API planned | Weights + Audar API | Weights + Cohere API | Weights + DashScope API |
Sources: Falcon-ASR figures from TII’s blog. Audar, Cohere and Qwen leaderboard figures from the ELM leaderboard as reproduced on the Audar card. Emirati WER for all models is from TII’s internal evaluation.
Key Takeaways
- Falcon-ASR is a 1.6B Arabic-first speech model from TII with an Emirati focus.
- It reports 20.92% average Arabic WER, 2.25 points ahead of Audar-ASR-V1-Turbo.
- On TII’s Emirati set it beats a 30B Qwen3-Omni by 4.07 WER points.
- 1 set of weights covers 5 languages, with word-level timestamps.
- No weights, license or API yet: test it via the 60-second demo.
Check out the Falcon-ASR blog, the Falcon3-Audio paper and try the demo. Also,ย feel free to follow us onย Twitterย and donโt forget to join ourย 150k+ML SubRedditย and Subscribe toย our Newsletter. Wait! are you on telegram?ย now you can join us on telegram as well.
Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
