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--- |
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dataset_info: |
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features: |
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- name: audio |
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dtype: audio |
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- name: text |
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dtype: string |
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- name: cleaned_text |
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dtype: string |
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- name: environment_type |
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dtype: string |
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- name: speaker_id |
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dtype: string |
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- name: speaker_gender |
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dtype: string |
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splits: |
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- name: test |
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|
num_bytes: 2702664819.072 |
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num_examples: 3296 |
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download_size: 2564183776 |
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dataset_size: 2702664819.072 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/test-* |
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task_categories: |
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- automatic-speech-recognition |
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- text-to-speech |
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language: |
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- ar |
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pretty_name: SCC 2022 |
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size_categories: |
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- 1K<n<10K |
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license: cc-by-nc-sa-4.0 |
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--- |
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<center> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6116d0584ef9fdfbf45dc4d9/5g0ERAppFLxQhXU2Haj_X.png"/> |
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</center> |
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# Dataset Card for Saudilang Code-Switch Corpus (SCC) |
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## Dataset Summary |
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The **Saudilang Code-Switch Corpus (SCC)** is a **5-hour** transcribed audio dataset featuring informal Saudi Arabic speech with **code-switching to English**, sourced from the **"Thmanyah" YouTube podcast**. It was created for evaluating models in multilingual and dialectal speech settings, such as **ASR**, **language identification**, and **code-switch detection**. |
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## Supported Tasks and Leaderboards |
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This dataset is suitable for testing models in: |
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* **Automatic Speech Recognition (ASR)** |
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* **Text-to-Speech (TTS)** |
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* **Speaker Diarization** |
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* **Language Identification** |
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* **Dialect Detection** |
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* **Code-Switching Detection** |
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## Languages |
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* **Arabic (ar)** – primarily Saudi dialect |
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* **English (en)** – code-switched within Arabic speech |
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## Dataset Structure |
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### Data Fields |
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* `audio`: The raw audio recording (e.g., `.wav`) |
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* `text`: The original transcription including both Arabic and English content |
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* `cleaned_text`: A normalized version of the transcription |
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* `environment_type`: Acoustic environment in which the speech was recorded (Clean, Music, or Noisy) |
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* `speaker_id`: An anonymized identifier for the speaker |
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* `speaker_gender`: Gender of the speaker (all of them are Male) |
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### Splits |
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* **Test Set Only**: \~5 hours of annotated, transcribed speech |
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*(This dataset is designed primarily for evaluation purposes.)* |
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## Dataset Creation |
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### Source and Curation |
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The dataset was curated from naturally occurring podcast conversations and **transcribed by the National Center for Artificial Intelligence at SDAIA**. |
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### Motivation |
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Given the scarcity of high-quality Arabic-English code-switching resources, this dataset was developed to address that gap and enable progress in speech technologies for bilingual Arabic users. Publishing SCC demonstrates a commitment to enriching Arabic digital resources and enabling the development of AI models that are more linguistically inclusive and reflective of real-world usage. |
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## Licensing |
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This dataset is licensed under the **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)** license. |
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More details: [https://creativecommons.org/licenses/by-nc-sa/4.0/](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
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## Citation |
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``` |
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@misc{SCC2025, |
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title={Saudilang Code-Switch Corpus (SCC)}, |
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author={SDAIA}, |
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year={2022}, |
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howpublished={\url{https://www.kaggle.com/datasets/sdaiancai/saudilang-code-switch-corpus-scc}}, |
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note={CC BY-NC-SA 4.0} |
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} |
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``` |
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## Contributions |
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Dataset curated by **SDAIA**. Dataset card written by the open-source community. |
