Datasets:

retkowski
/

ytseg

@@ -14,7 +14,7 @@ size_categories:
 # From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions
-We present <span style="font-variant:small-caps; font-weight:700;">YTSeg</span>, a topically and structurally diverse benchmark for the text segmentation task based on YouTube transcriptions. The dataset comprises 19,299 videos from 393 channels, amounting to 6,533 content hours. The topics are wide-ranging, covering domains such as science, lifestyle, politics, health, economy, and technology. The videos are from various types of content formats, such as podcasts, lectures, news, corporate events \& promotional content, and, more broadly, videos from individual content creators. We refer to the **paper** ([acl](https://aclanthology.org/2024.eacl-long.25/) | [arXiv](https://arxiv.org/abs/2402.17633)) for further information.
 ## Data Overview
@@ -57,7 +57,11 @@ Each chapter of a video is represented as a JSON object with the following field
 | Testing    | 13,082 (7.6%)  |
 | Total      | 173,195       |
-### Video & Audio Data
 A download script for the video and audio data is provided.
@@ -67,9 +71,9 @@ python download_videos.py
 In the script, you can further specify a target folder (default is `./video`) and target formats in a priority list.
-## Loading Data
-This repository comes with a simple, exemplary script to read in the data with `pandas`.
 ```py
 from load_data import get_partition

 # From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions
+We present <span style="font-variant:small-caps; font-weight:700;">YTSeg</span>, a topically and structurally diverse benchmark for the text segmentation task based on YouTube transcriptions. The dataset comprises 19,299 videos from 393 channels, amounting to 6,533 content hours. The topics are wide-ranging, covering domains such as science, lifestyle, politics, health, economy, and technology. The videos are from various types of content formats, such as podcasts, lectures, news, corporate events \& promotional content, and, more broadly, videos from individual content creators. We refer to the **paper** ([acl](https://aclanthology.org/2024.eacl-long.25/) | [arXiv](https://arxiv.org/abs/2402.17633)) for further information. We provide both text and audio data as well as a download script for the video data.
 ## Data Overview
 | Testing    | 13,082 (7.6%)  |
 | Total      | 173,195       |
+### Audio Data
+We provide audio files for all examples in the dataset, preprocessed into the .mp3 format with a standardized sample rate of 16,000 Hz and a single channel (mono). These files are organized within the directory structure as follows: `data/audio/<channel_id>/<video_id>.mp3`.
+### Video Data
 A download script for the video and audio data is provided.
 In the script, you can further specify a target folder (default is `./video`) and target formats in a priority list.
+## Loading Text Data
+This repository comes with a simple, exemplary script to read in the text data with `pandas`.
 ```py
 from load_data import get_partition

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