metadata

license: cc-by-nc-sa-4.0
task_categories:
  - automatic-speech-recognition
language:
  - multilingual
pretty_name: CS-Dialogue
size_categories:
  - 10K<n<100K
features:
  - name: audio
    dtype: audio
  - name: transcription
    dtype: string
  - name: speaker
    dtype: string
  - name: gender
    dtype: string
  - name: age
    dtype: string
  - name: location
    dtype: string
  - name: device
    dtype: string
extra_gated_prompt: >-
  This dataset is made available for academic and non-commercial research
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  following terms and conditions:  

  1. The dataset may only be used for academic research and educational
  purposes. Any commercial use, including but not limited to commercial product
  development, commercial speech recognition services, or monetization of the
  dataset in any form, is strictly prohibited.  

  2. The dataset must not be used for any research or applications that may
  infringe upon the privacy rights of the recorded participants. Any attempt to
  re-identify participants or extract personally identifiable information from
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extra_gated_fields:
  Name: text
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  Affiliation: text
  Position: text
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  I agree to the Terms of Access: checkbox
  I agree to use this dataset for non-commercial use ONLY: checkbox

CS-Dialogue: A 104-Hour Dataset of Spontaneous Mandarin-English Code-Switching Dialogues for Speech Recognition

Introduction

CS-Dialogue is a large-scale, publicly available Mandarin-English code-switching speech dialogue dataset. This dataset solves key problems found in existing code-switching speech datasets — mainly their small size, lack of natural conversations, and missing full-length dialogue recordings. It provides a solid foundation for advancing research in code-switching ASR and other related fields. The dataset is released under a CC BY-NC-SA 4.0 license, meaning it is available for non-commercial use.

Dataset Details

This dataset contains 104.02 hours of spontaneous dialogue recordings, consisting of 100 pairs of two-person conversations recorded by 200 speakers. Key features of the dataset include:

Speakers: 200 speakers with strong English proficiency (e.g., IELTS ≥ 6 or passing TEM-4).
Geographic Diversity: Speakers come from 30 provincial-level regions across mainland China.
Content: Each conversation covers 2 to 6 topics and includes Mandarin-only, code-switching, and English-only segments.
Audio Format: WAV files with a 16kHz sampling rate.
Transcriptions: Carefully crafted, character-level manual transcriptions.
Annotations: The dataset includes annotations for each utterance, and for the speakers level.
- Utterance-level: id, audio (file path), text (transcription).
- Speaker-level: speaker_id, age, gender, location (province), device.

Dataset Structure

The dataset file structure is as follows.

data
├── long_wav/*.tar.gz
├── short_wav/*.tar.gz
└── index
    ├── long_wav
    │   ├── dev.txt
    │   ├── test.txt
    │   └── train.txt
    ├── short_wav
    │   ├── dev
    │   │   ├── text
    │   │   └── wav.scp
    │   ├── test
    │   │   ├── text
    │   │   └── wav.scp
    │   └── train
    │       ├── text
    │       └── wav.scp
    └── total_infomation
        └── Information_Index.txt

For more details, please refer to our paper CS-Dialogue.

📚 Cite me

@article{zhou2025cs,
  title={CS-Dialogue: A 104-Hour Dataset of Spontaneous Mandarin-English Code-Switching Dialogues for Speech Recognition},
  author={Zhou, Jiaming and Guo, Yujie and Zhao, Shiwan and Sun, Haoqin and Wang, Hui and He, Jiabei and Kong, Aobo and Wang, Shiyao and Yang, Xi and Wang, Yequan and others},
  journal={arXiv preprint arXiv:2502.18913},
  year={2025}
}