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YouTube Chapters (YTC) Dataset

The YouTube Chapters (YTC) dataset is specially curated for long tutorial videos. This dataset focuses on the annotations for YouTube chapters, which provide structured segmentations of the video content.

Data Splits

All the data splits (train.json, test.json, and val.json) contain chapter annotations. These annotations are extracted directly from YouTube and have been manually annotated by the video creators themselves, ensuring the authenticity and relevance of each chapter.

Pretraining Data

For those interested in pretraining models, this dataset also includes a file named pretrain_bmd.jsonl. This file contains data used for the pretraining phase. For a comprehensive understanding of how this data was sourced and processed, please refer to the Behance Moment Detection (BMD) dataset.

Video Links Association

To correlate the video_id from the annotations with the actual video link, you can refer to the ytc_links.jsonl file.

Annotation File Structure

The dataset annotations are formatted in the jsonl (JSON Lines) format. Each line in the annotation file is a standalone JSON object, detailing a specific moment in a video.

Here's an example of how the annotations look:

{"vid": "-IXKOGg_Vos", "query": " \u2013 Introduction", "qid": "youtube_-IXKOGg_Vos_1", "relevant_windows": [[34, 109]]}

Attributes:

  • qid: The unique query identifier.
  • query: The actual query described in natural language, summarizing the video segment's content.
  • vid: The video identifier, which can be used to reference the specific video.
  • relevant_windows: An array containing the start and end times (in seconds) of the relevant video segment for the described query.

Weights

The YTC dataset weights can be downloaded from here.

The BMD pretrained weights can be downloaded from here.

Videos

We've released the videos for this dataset. Each tar.gz file from the collection contains frames extracted at 1 fps from a single video.

You can download the videos from the following links:

YTC videos

To download the videos programmatically, install the internetarchive package

pip install internetarchive

To inspect the contents of the collection, use:

ia metadata ytc_videos

To download all the videos, use:

ia download ytc_videos

Alternatively, each video can be downloaded independently, using:

ia download ytc_videos {video_id}.tar.gz

Extract videos

When you unzip a video, it will directly extract all frames into the current working directory. To organize the frames, you may prefer to create a separate directory for each video. You can do so by using the following commands:

mkdir {video_id}
tar -xzvf {video_id}.tar.gz -C {video_id}

If you prefer to convert the .tar.gz file to a .tar file, use the following command:

gunzip {video_id}.tar.gz

Features

We provide the Slowfast features and GPT2 features.

To download the features programmatically, use:

pip install internetarchive
ia download ytc_features