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license: cc-by-nc-2.0
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---
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license: cc-by-nc-2.0
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language:
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- ar
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- en
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tags:
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- Arabic
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- English
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- Broadcast
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- Code-switching
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- Punctuation
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pretty_name: 'QASR: QCRI Aljazeera Speech Resource'
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---
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# QASR: QCRI Aljazeera Speech Resource
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**QASR** is the largest transcribed Arabic speech corpus with around **2,000 hours** of data.
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It features **multi-layer annotation**, covering **multiple Arabic dialects** and **code-switching** speech.
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---
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## 📘 Overview
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QASR is a large-scale transcribed Arabic speech corpus collected from **Aljazeera News Channel** broadcasts.
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The data is **lightly supervised** and **linguistically segmented**, designed to support a wide range of speech and language processing research tasks.
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### Key Features
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- ~2,000 hours of transcribed Arabic speech
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- Multi-dialect and code-switching coverage
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- Multi-layer linguistic annotations
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- Lightly supervised transcriptions
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- Linguistically motivated segmentation
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---
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## 📄 Lisence
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Non-Commercial Purpose ONLY!
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---
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## 📥 Download
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You can request or download the dataset using the link below:
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👉 **[Download QASR Dataset](https://forms.gle/4U34R3Sy9xcmtuRw8)**
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Please follow the instructions on the linked page to complete the request process and download the data.
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---
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## 🧠 Applications
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QASR is suitable for training and evaluating:
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- **Automatic Speech Recognition (ASR)** systems
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- **Arabic Dialect Identification** (acoustics- and linguistics-based)
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- **Punctuation Restoration**
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- **Speaker Identification** and **Speaker Linking**
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- **Spoken Language Understanding** and other **NLP modules** for spoken data
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---
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## 📊 Data Source
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The corpus was **crawled from the Aljazeera news channel**, providing rich diversity in topics, speakers, and dialectal variation.
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---
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## 📄 Citation
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If you use QASR in your research, please cite:
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```bibtex
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@inproceedings{mubarak_qasr_2021,
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title = {{QASR}: {QCRI} {Aljazeera} {Speech} {Resource}. {A} {Large} {Scale} {Annotated} {Arabic} {Speech} {Corpus}},
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booktitle = {{Proc. of ACL}},
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author = {Mubarak, Hamdy and Hussein, Amir and Chowdhury, Shammur Absar and Ali, Ahmed},
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year = {2021},
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}
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