Datasets:

MohamedRashad
/

SCC22

dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: text
      dtype: string
    - name: cleaned_text
      dtype: string
    - name: environment_type
      dtype: string
    - name: speaker_id
      dtype: string
    - name: speaker_gender
      dtype: string
  splits:
    - name: test
      num_bytes: 2702664819.072
      num_examples: 3296
  download_size: 2564183776
  dataset_size: 2702664819.072
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
task_categories:
  - automatic-speech-recognition
  - text-to-speech
language:
  - ar
pretty_name: SCC 2022
size_categories:
  - 1K<n<10K
license: cc-by-nc-sa-4.0

Dataset Card for Saudilang Code-Switch Corpus (SCC)

Dataset Summary

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.

Supported Tasks and Leaderboards

This dataset is suitable for testing models in:

Automatic Speech Recognition (ASR)
Text-to-Speech (TTS)
Speaker Diarization
Language Identification
Dialect Detection
Code-Switching Detection

Languages

Arabic (ar) – primarily Saudi dialect
English (en) – code-switched within Arabic speech

Dataset Structure

Data Fields

audio: The raw audio recording (e.g., .wav)
text: The original transcription including both Arabic and English content
cleaned_text: A normalized version of the transcription
environment_type: Acoustic environment in which the speech was recorded (Clean, Music, or Noisy)
speaker_id: An anonymized identifier for the speaker
speaker_gender: Gender of the speaker (all of them are Male)

Splits

Test Set Only: ~5 hours of annotated, transcribed speech (This dataset is designed primarily for evaluation purposes.)

Dataset Creation

Source and Curation

The dataset was curated from naturally occurring podcast conversations and transcribed by the National Center for Artificial Intelligence at SDAIA.

Motivation

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.

Licensing

This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. More details: https://creativecommons.org/licenses/by-nc-sa/4.0/

Citation

@misc{SCC2025,
  title={Saudilang Code-Switch Corpus (SCC)},
  author={SDAIA},
  year={2022},
  howpublished={\url{https://www.kaggle.com/datasets/sdaiancai/saudilang-code-switch-corpus-scc}},
  note={CC BY-NC-SA 4.0}
}

Contributions

Dataset curated by SDAIA. Dataset card written by the open-source community.