Datasets:

BAAI
/

Chinese-LiPS

license: cc-by-nc-sa-4.0
task_categories:
  - automatic-speech-recognition
language:
  - zh
pretty_name: Chinese-LiPS
configs:
  - config_name: default
    data_files:
      - split: train
        path: meta_train.csv
      - split: valid
        path: meta_valid.csv
      - split: test
        path: meta_test.csv
extra_gated_prompt: >-
  This dataset is made available for academic and non-commercial research
  purposes only. By accessing or using the dataset, you agree to comply with the
  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
  the dataset is strictly prohibited. Researchers must ensure that their use of
  the dataset aligns with ethical research practices and institutional review
  board (IRB) guidelines where applicable.  

  3. If a participant (or their legal guardian) requests the removal of their
  data from the dataset, all recipients of the dataset must comply by deleting
  the affected data from their records. Researchers must acknowledge that such
  withdrawal requests may occur and agree to take reasonable steps to ensure
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  4. The dataset, in whole or in part, may not be redistributed, resold, or
  shared with any third party. Each researcher or institution must independently
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  5. Any published work (e.g., papers, reports, presentations) that uses this
  dataset must properly cite the dataset as specified in the accompanying
  documentation.  

  6. You may create derived works (e.g., processed versions of the dataset) for
  research purposes, but such derivatives must not be distributed beyond your
  research group without prior written permission from the dataset
  maintainers.  

  7. The dataset is provided "as is" without any warranties, express or implied.
  The dataset maintainers are not responsible for any direct or indirect
  consequences arising from the use of the dataset.  

  8. Failure to comply with these terms may result in revocation of dataset
  access. The dataset maintainers reserve the right to deny access to any
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  9. If the researcher is employed by a for-profit, commercial entity, the
  researcher's employer shall also be bound by these terms and conditions, and
  the researcher hereby represents that they are fully authorized to enter into
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  By requesting access to the dataset, you acknowledge that you have read,
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extra_gated_fields:
  Name: text
  Email: text
  Affiliation: text
  Position: text
  Your Supervisor/manager/director: text
  I agree to the Terms of Access: checkbox
  I agree to use this dataset for non-commercial use ONLY: checkbox
size_categories:
  - 10K<n<100K

Chinese-LiPS: A Chinese audio-visual speech recognition dataset with Lip-reading and Presentation Slides

⭐ Introduction

The Chinese-LiPS dataset is a multimodal dataset designed for audio-visual speech recognition (AVSR) in Mandarin Chinese. This dataset combines speech, video, and textual transcriptions to enhance automatic speech recognition (ASR) performance, especially in educational and instructional scenarios.

🚀 Dataset Details

Total Duration: 100.84 hours
Number of Speakers: 207 professional speakers
Number of Clips: 36,208 video clips
Audio Format: Stereo WAV, 48 kHz sampling rate
Video Format:
- Slide Video: 1080p resolution, 30 fps
- Lip-Reading Video: 720p resolution, 30 fps
Annotations: JSON format with transcriptions and extracted text from slides

Dataset Statistics

Split	Duration (hrs)	# Segments	# Speakers
Train	85.37	30,341	175
Validation	5.35	1,959	11
Test	10.12	3,908	21
Total	100.84	36,208	207

📂 Dataset Organization

The dataset is structured into several compressed files:

image.zip: First-frame images from slide videos (used for OCR and vision-language models).
processed_test.zip processed_val.zip processed_train.zip: Processed data with 16 kHz audio, 96×96 25-frame lip-reading videos, and JSON annotations.

train.zip, test.zip, val.zip: Data split into training, testing, and validation sets. Each contains:

├── ID1_age_gender_topic/
│   ├── WAV/
│   │   ├── ID1_age_gender_topic_001.json  # Annotation file
│   │   ├── ID1_age_gender_topic_001.wav   # Audio file (48 kHz)
│   ├── PPT/
│   │   ├── ID1_age_gender_topic_001_PPT.mp4  # Slide video (1080p 30fps)
│   ├── FACE/
│   │   ├── ID1_age_gender_topic_001_FACE.mp4  # Lip-reading video (720p 30fps)
├── ...

meta_all.csv, meta_train.csv, meta_valid.csv, meta_test.csv: Metadata files with ID, TOPIC, WAV, PPT, FACE, and TEXT fields.

The TOPIC field is abbreviated in Chinese as follows: DZJJ = E-sports & Gaming, JKYS = Health & Wellness, KJ = Science & Technology, LY = Travel & Exploration, QC = Automobile & Industry, RWLS = Culture & History, TY = Sports & Competitions, YS = Movies & TV Series, ZX = Others.

meta_test.json: Includes OCR and InternVL2 prompts for the test set.

wav_path: Path to the audio file.
ppt_path: Path to the first-frame image of the slide video.
ocr_text: Text extracted by PaddleOCR.
vl2_text: Text extracted by InternVL2.
gt_text: Ground truth transcription of the audio.
ocr_vl2_text: OCR text reprocessed by InternVL2 (not a concatenation of PaddleOCR and InternVL2 results).

📥 Download

You can download the dataset from the following sources:

Download from OneDrive
Download from Huggingface
Download from Baidu Netdisk (Password: vg2a)

📚 Citation

@misc{zhao2025chineselipschineseaudiovisualspeech,
  title={Chinese-LiPS: A Chinese audio-visual speech recognition dataset with Lip-reading and Presentation Slides}, 
  author={Jinghua Zhao and Yuhang Jia and Shiyao Wang and Jiaming Zhou and Hui Wang and Yong Qin},
  year={2025},
  eprint={2504.15066},
  archivePrefix={arXiv},
  primaryClass={cs.MM},
  url={https://arxiv.org/abs/2504.15066}
}