TII Releases Falcon-ASR: 1.6B Arabic Speech Model Hits 20.92% WER

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, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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