Add initial dataset card for StreamingVLM datasets

README.md

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+---
+task_categories:
+- video-text-to-text
+language:
+- en
+tags:
+- video-understanding
+- streaming-video
+- real-time
+- long-video
+- vision-language-model
+---
+
+# StreamingVLM Datasets
+
+This repository contains datasets used in the paper [StreamingVLM: Real-Time Understanding for Infinite Video Streams](https://huggingface.co/papers/2510.09608).
+
+StreamingVLM introduces a model designed for real-time, stable understanding of infinite visual input. It addresses challenges like escalating latency and memory usage in processing long video streams by maintaining a compact KV cache and aligning training with streaming inference. The project includes novel datasets for both training and evaluation, particularly `Inf-Streams-Eval`, a new benchmark with videos averaging over two hours that requires dense, per-second alignment between frames and text.
+
+- **Paper**: [https://huggingface.co/papers/2510.09608](https://huggingface.co/papers/2510.09608)
+- **Code**: [https://github.com/mit-han-lab/streaming-vlm](https://github.com/mit-han-lab/streaming-vlm)
+- **Project/Demo Page**: [https://streamingvlm.hanlab.ai](https://streamingvlm.hanlab.ai)
+
+## Included Datasets
+
+The datasets associated with the StreamingVLM project include:
+
+*   **Inf-Stream-Train**: This dataset is used for supervised fine-tuning (SFT) of the StreamingVLM model.
+*   **Live-WhisperX-526K**: An additional dataset utilized during the SFT process, described as `Livecc_sft` in the project's setup.
+*   **Inf-Stream-Eval**: A new benchmark dataset introduced for evaluating real-time video understanding, featuring long videos (averaging over two hours) and requiring dense, per-second alignment between frames and text.
+
+## Sample Usage: Dataset Preparation for SFT
+
+To prepare the necessary datasets for Supervised Fine-Tuning (SFT) as described in the [GitHub repository](https://github.com/mit-han-lab/streaming-vlm):
+
+First, download `mit-han-lab/Inf-Stream-Train` to `/path/to/your/Inf-Stream-Train`.
+Then, download `chenjoya/Live-WhisperX-526K` to `/path/to/your/Inf-Stream-Train/Livecc_sft`.
+Preprocess the LiveCC dataset with the following command:
+
+```bash
+cd $DATASET_PATH/Livecc_sft
+find . -type f -exec mv -t . {} +
+```
+
+Download `mit-han-lab/Inf-Stream-Eval` to `/path/to/your/Inf-Stream-Eval`.
+
+Finally, set environment paths:
+
+```bash
+export DATASET_PATH=/path/to/your/Inf-Stream-Train
+export EVAL_DATASET_PATH=/path/to/your/Inf-Stream-Eval
+```
+
+## Citation
+
+If you find StreamingVLM useful or relevant to your project and research, please kindly cite our paper:
+
+```bibtex
+@misc{xu2025streamingvlmrealtimeunderstandinginfinite,
+      title={StreamingVLM: Real-Time Understanding for Infinite Video Streams}, 
+      author={Ruyi Xu and Guangxuan Xiao and Yukang Chen and Liuning He and Kelly Peng and Yao Lu and Song Han},
+      year={2025},
+      eprint={2510.09608},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2510.09608}, 
+}
+```