metadata

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

Dataset Description

Curated by: Joya Chen
Language(s) (NLP): English
License: Apache License 2.0

Uses

This dataset is used for LiveCC-7B-Base 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.

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.

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:

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

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

Citation

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

@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