README.md

---
language:
- en
license: apache-2.0
size_categories:
- 1M<n<10M
task_categories:
- video-text-to-text
configs:
- config_name: Live-CC-5M for Dataset Viewer
  data_files:
  - split: preview_first_100
    path: live_cc_100_for_preview.json
  - split: full_5m
    path: live_cc_5m_with_seeks.jsonl
---

# Dataset Card for Live-CC-5M

![image/png](https://cdn-uploads.huggingface.co/production/uploads/642435a1a3adbc7142c3b0a6/8UHcUg6ZsSyyPn6tpZve1.png)

## Dataset Description
- **Curated by:** Joya Chen
- **Language(s) (NLP):** English
- **License:** Apache License 2.0

## Uses
This dataset is used for [LiveCC-7B-Base](https://huggingface.co/chenjoya/LiveCC-7B-Instruct) model pre-training. We only allow the use of this dataset for academic research and educational purposes. For OpenAI GPT-4o generated user prompts, we recommend users check the [OpenAI Usage Policy](https://openai.com/policies/usage-policies/).

- **Project Page**: https://showlab.github.io/livecc
- **Paper**: https://huggingface.co/papers/2504.16030

### Live-CC-5M Dataset
  
  - Statistics: 5,047,208 YouTube Video-CC 30~240s samples.
    ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642435a1a3adbc7142c3b0a6/-RR-sI7F1a1XpxuQad2DH.png)

  - Annotation JSONL (YouTube CC): 

    Each line of the JSONL file is organized in a common user/assistant conversation format with a special "text_stream" key. Example:
    ```
    [
      {"role": "user", "content": [{"type": "video", "video": "video/youtube/-4dnPeRv1ns.mp4", "video_start": 16.8, "video_end": 158.8}, {"type": "text", "text": "", "previous": "", "title": "Airsoft G&G Combat Machine M4 Review"}]},
      {"role": "assistant", "content": [{"type": "text_stream", "text_stream": [[16.8, 16.9, "all"], [16.9, 17.0, "right"], [17.0, 17.1, "you"], [17.1, 17.3, "guys"], [17.3, 17.4, "so"], [17.4, 17.5, "this"], ...]}]}
    ]
    ```
    - "title" denotes the YouTube title.
    - "previous" denotes previous ASR content before "video_start".
    - Each item in "text_stream" indicates start timestamp, end timestamp, and the word.

    During pre-training, we use "title" and "previous" as context. Please refer to our dataloader (https://github.com/showlab/livecc/data/lmm_dataset.py) to learn how to make it compatible with popular LMMs (e.g. QwenVL series).
  
    The last line of JSONL contains the file handle seek indices:
    ```
    b'[0, 3149, 7796, 10436, 18949, 22917, 41985, 65721, 73045, 76797, 82262, ...]'
    ```
    This allows for easy streaming loading access using:
    
    ```python
    import json

    # read the last line of jsonl
    def readlastline(path: str):
      with open(path, "rb") as f:
          f.seek(-2, 2) # avoid last 

          while f.read(1) != b"\n":  
              f.seek(-2, 1)
          return f.readline()
    
    # parse to seek indices list
    seeks = json.loads(readlastline('live_cc_5m_with_seeks.jsonl'))
    
    # during data loader
    def __getitem(self, index):
      ...
      with open('live_cc_5m_with_seeks.jsonl') as f:
        f.seek(seeks[index])
        datum = json.loads(f.readline())
      ...
    ```
  
  - Videos: Due to 5M videos are too large, we are sorry that we cannot find way to share them. But,
    - You can find all YouTube IDs in the annotation JSONL
    - We have released video files in SFT dataset https://huggingface.co/datasets/chenjoya/Live-WhisperX-526K


### Data Production Pipeline

![image/png](https://cdn-uploads.huggingface.co/production/uploads/642435a1a3adbc7142c3b0a6/vQc91ksNXBeYALvKvcN8y.png)

Please read the paper Section3 for details. They have been fully open-sourced at: https://github.com/showlab/livecc/tree/main/data/production

## Citation

If you find our work helpful, feel free to give us a cite ;)

```bibtex
@article{livecc,
  author       = {Joya Chen and Ziyun Zeng and Yiqi Lin and Wei Li and Zejun Ma and Mike Zheng Shou},
  title        = {LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale},
  journal      = {arXiv preprint arXiv:2504.16030}
  year         = {2025},
}
```

## Contact

[Joya Chen](https://chenjoya.github.io/)