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README.md
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---
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configs:
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- config_name: Live-CC-5M for Dataset Viewer
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data_files:
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- split: preview_first_100
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path: live_cc_100_for_preview.json
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- split: full_5m
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path: live_cc_5m_for_preview.json
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license: apache-2.0
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task_categories:
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- video-text-to-text
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language:
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- en
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size_categories:
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- 1M<n<10M
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---
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# Dataset Card for LiveCC-WhisperX-528K
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## Dataset Description
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- **Curated by:** Joya Chen
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- **Language(s) (NLP):** English
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- **License:** Apache License 2.0
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## Uses
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This dataset is used for the training of the [LiveCC-7B-Base](https://huggingface.co/chenjoya/LiveCC-7B-Instruct) model. 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/).
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- **Project Page**: https://showlab.github.io/livecc/
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- **Paper**: https://arxiv.org/abs/xxxx.xxxxx
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- **Training Code**: https://www.github.com/showlab/livecc
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### Data Sources
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- **Live-CC-5M**: This repository includes
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- Annotation JSONL (YouTube CC): https://huggingface.co/datasets/chenjoya/Live-CC-5M/blob/main/live_cc_5m_with_seeks.jsonl
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Due to 5M videos are too large, we are sorry that we cannot provide them. But,
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- You can find all YouTube keys in the annotation JSONL
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- We have released video files in SFT set: https://huggingface.co/datasets/chenjoya/Live-WhisperX-528K
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This dataset contains 5,047,208 YouTube Video-CC instances, with YouTube categories:
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```
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{'People & Blogs': 1130380, 'Howto & Style': 889359, 'Entertainment': 585838, 'Education': 453305, 'Sports': 449166, 'Autos & Vehicles': 410228, 'Science & Technology': 345308, 'Film & Animation': 197529, 'Travel & Events': 151357, 'Pets & Animals': 127535, 'Gaming': 117149, 'News & Politics': 83560, 'Nonprofits & Activism': 59140, 'Comedy': 47354}
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```
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Each line of the JSONL file is organized in a common user/assistant conversation format with a special "text_stream" key. Example:
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```
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[
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{"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"}]},
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{"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"], ...]}]}
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]
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```
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"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).
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The last line of JSONL contains the file handle seek indices:
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```
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[0, 3149, 7796, 10436, 18949, 22917, 41985, 65721, 73045, 76797, 82262, ...]
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```
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This allows for easy streaming access using:
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```
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with open('live_whisperx_528k_with_seeks.jsonl') as f:
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f.seek(seek_index)
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datum = json.loads(f.readline())
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```
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### Data Pipeline
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Please refer to Section 3 of our [paper](https://arxiv.org/abs/xxxx.xxxxx). It has been fully open-sourced at: https://github.com/showlab/livecc/data/production/pretrain
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## Citation
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```bibtex
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@inproceedings{livecc,
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author = {Joya Chen and Ziyun Zeng and Yiqi Lin and Wei Li and Zejun Ma and Mike Zheng Shou},
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title = {LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale},
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booktitle = {CVPR},
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year = {2025},
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}
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```
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## Dataset Card Contact
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[Joya Chen](https://chenjoya.github.io/)
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