阿布扎比技术创新研究所(TII)拥有发布 Falcon-ASR，一个1.6B参数阿拉伯语语音识别模型。 其重点是埃米拉蒂方言，同时也翻译英语，法语，西班牙语和葡萄牙语。 Falcon-ASR在TII的评价中，这比TII领导板快照中公布的最佳条目高出2.25分。 对于建设阿拉伯语语音产品的开发者来说，捕获就是访问：今天它只作为主机演示运行。
TL；数据
- 大小：1.6B参数。 最大音频上下文：未披露(演示可接受最多60秒和20MB的剪辑).
- 运行时间：未披露。 没有公布加权或定量变种。 访问是 Hugging Face 演示，计划有 API 和本土应用。
- 性能：TII自身比较中最低的阿拉伯语和埃米尔派WER，有坚实的英语结果。
- 最佳：20.92%的阿拉伯WER，比奥达-ASR-V1-Turbo低2.25分(23.17%)。
- 最差：11.86%的WER 在Earnings-22，其最弱的英语测试集。
- 底线(最佳)：在TII的测试中，采用1.6B型紧凑型，领先较大的阿拉伯系统。
- 底线(最差)：没有权重，没有许可证，还没有API，所有的分数都是自报的。
什么是猎鹰ASR?
Falcon-ASR是一种来自TII的语音转文本模型，将口语阿拉伯语和其他4种语言转换为文本。 所有5种语言共用一组权重。 该模式不需要语言旗帜和所讲语言的产出文本。 它还返回词级时间戳，将每个词与它在音频中的位置联系起来。 这对字幕、可搜索录音和会见指出很重要。
Falcon-ASR如何处理阿拉伯语方言？
与现代标准阿拉伯语(MSA)相比，Dialectal阿拉伯语的转写资源要少得多。 TII对Falcon-ASR进行了关于阿联酋语、MSA、其他海湾和阿拉伯语方言以及英语的培训。 目标是日常的语音，包括方言形式和语言切换。
培训的音频还增加了背景噪音、重复的演讲、音乐、房间反响和电话效应。 速度和投球率也各不相同。 对阿联酋的录音也采用了同样的处理办法。
该模型基于TII的Falcon3-Audio作品。 该研究描述了基于指令调制的LLMs和Whisper编码器的音频语言模型，在不到30K小时的公共音频上接受了培训。 我们Marktechpost的编辑和技术团队找不到任何出版的Falcon-ASR确切的架构或训练数据大小。
Falcon-ASR在阿拉伯语基准测试上得分如何？.
TII遵循了由ELM研究中心运行的开放通用阿拉伯ASR Leaderboard的协议。 它以等重平均WER在6套测试中排出系统。 TII使用领标板的标注单。 竞争对手人数于2026年9月30日公布领导板平均值检查。
低点更好请注意，Falcon-ASR 的数字是 TII 自己的跑步，而不是领跑板条目。 奥达模式卡列出自身平均CER为9.20%.
Falcon-ASR如何在阿联酋演讲中表现？
TII用人类验证的录音记录制作了阿联酋和海湾内部测试它是对卡萨布兰卡公共数据集阿联酋子集的补充。
Falcon-ASR领先4.07 WER在Qwen3-Omni上空点球。 这个测试集是私有的，所以无法复制。 Audar)在不同的埃米尔集(Mixat)上报告了19.4%的WER，因此数字并不直接可比。
Falcon -ASR在英语方面有多好？
在Hugging Face Open ASR Leaderboard使用的7套英语测试中，TII报告为5.74%的WER. 每套得分从LibriSpeech干净的1.75%到Earnings-22的11.86%不等。 对于上下文，Qwen3-ASR-1.7B列出了5.76%的WER在该领导板上。 法语，西班牙语和葡萄牙语获得支持，但TII没有为他们发布分数。
比较：Falcon-ASR与最接近的阿拉伯ASR模型
来源：TII博客的Falcon-ASR数字。 Audar),Cohere和Qwen领头板人物从ELM领头板复制到Audar卡上。 所有模型都来自TII的内部评价。
关键外卖
- 猎鹰ASR是1.6B TII的阿拉伯语第一演说模式
- 报告平均20.92%的阿拉伯WER，比奥达尔-ASR-V1-Turbo领先2.25分。
- 在TII的埃米尔派中
- 1套权重包括5种语言，带有词级时间戳。
还没有权重、执照或API：通过60秒的演示测试。
看看Falcon-ASR博客，Falcon3-Audio的论文并尝试演示。 并加入我们的通讯。 等等你在电报里吗现在你也可以加入我们的电报。
Asif Razzaq是Marktechpost AI Media Inc.的首席执行官。 作为一个有远见的企业家和工程师，阿西夫致力于利用人工智能的潜力促进社会福祉。 他最近的努力是人工智能媒体平台的发布，该平台以深入报道机器学习和深层学习新闻而著称，这些新闻在技术上既可靠又为广大观众所容易理解。 该平台每月有超过200万次观看，表明其受观众欢迎。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
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.
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.
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
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.
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.
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