Update README.md

README.md

@@ -1,3 +1,117 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+# Dataset Card for the RealTalk Video Dataset
+
+Thank you for your interest in the RealTalk dataset! RealTalk consists of 692 in-the-wild videos of dyadic (i.e. two person) conversations, curated with the goal of advancing multimodal communication research in computer vision.
+If you find our dataset useful, please cite
+```
+@inproceedings{geng2023affective,  
+title={Affective Faces for Goal-Driven Dyadic Communication},  
+author={Geng, Scott and Teotia, Revant and Tendulkar, Purva and Menon, Sachit and Vondrick, Carl},  
+year={2023}  
+}
+```
+
+---------------------------------------------------------------------------------------------------------------------------------------
+
+## Dataset Details
+
+The dataset contains 692 full-length videos scraped from [The Skin Deep](https://www.youtube.com/c/TheSkinDeep), a public YouTube channel that captures long-form, unscripted conversations between diverse indivudals about different facets of the human experience. We also include associated annotations; we detail all files present in the dataset below.
+
+### File Overview
+
+General notes: 
+* All frame numbers are indexed from 0. 
+* We denote 'p0' as the person on the left side of the video, and 'p1' as the person on the right side. 
+* <video_id> denotes the unique 11 digit video ID assigned by YouTube to a specific video.
+
+#### [0] videos/videos_{xx}.tar
+Contains the full-length raw videos that the dataset is created from in shards of 50. Each video is stored at 25 fps in ```avi``` format. 
+Each video is stored with filename ```<video_id>.avi``` (e.g., ```5hxY5Svr2aM.avi```).
+
+#### [1] audio.tar.gz
+Contains audio files extracted from the videos, stored in ```mp3``` format.
+
+#### [2] asr.tar.gz
+Contains ASR outputs of [Whisper](https://github.com/openai/whisper) for each video. Subtitles for video ```<video_id>.avi``` are stored in the file ```<video_id>.json``` as the dictionary
+```
+{
+	'text': <full asr transcript of video>
+	'segments': <time-stamped ASR segments>
+	'language': <detected language of video>
+}
+```
+
+#### [3.0] benchmark/train_test_split.json 
+This json file describes the clips used as the benchmark train/test split in our paper. The file stores the dictionary
+```
+{
+	'train': [list of train samples],
+	'test': [list of test samples]
+}
+```
+where each entry in the list is another dictionary with format
+```
+{
+	'id': [video_id, start_frame (inclusive), end_frame (exclusive)],
+	'speaker': 'p0'|'p1'
+	'listener': 'p0'|'p1'
+	'asr': str
+}
+```
+The ASR of the clip is computed with [Whisper](https://github.com/openai/whisper).
+
+
+#### [3.1] benchmark/embeddings.pkl 
+Pickle file containing visual embeddings of the listener frames in the training/testing clips, as computed by several pretrained face models implemented in [deepface](https://github.com/serengil/deepface). The file stores a dictionary with format
+```
+{
+	f'{video_id}.{start_frame}.{end_frame}:{
+		{
+			<model_name_1>: <array of listener embeddings>,
+			<model_name_2>: <array of listener embeddings>,
+			...
+		}
+	...
+}
+```
+
+#### [4] annotations.tar.gz
+Contains face bounding box and active speaker annotations for every frame of each video. Annotations for video ```<video_id>.avi``` are contained in file ```<video_id>.json```, which stores a nested dictionary structure:
+```
+{str(frame_number):{
+		'people':{
+			'p0':{'score': float, 'bbox': array}
+			'p1':{'score': float, 'bbox': array}
+		}
+		'current_speaker': 'p0'|'p1'|None
+	}
+	...
+}
+```
+The 'score' field stores the active speaker score as predicted by [TalkNet-ASD](https://github.com/TaoRuijie/TalkNet-ASD); larger positive values indicate a higher probability that the person is speaking. Note also that the 'people' subdictionary may or may not contain the keys 'p0', 'p1', depending on who is visible in the frame.
+
+#### [5] emoca.tar.gz
+
+Contains [EMOCA](https://emoca.is.tue.mpg.de/) embeddings for almost all frames in all the videos. The embeddings for```<video_id>.avi``` are contained in the pickle file ```<video_id>.pkl```, which has dictionary structure
+```
+{
+	int(frame_number):{
+		'p0': <embedding dict from EMOCA>,
+		'p1': <embedding dict from EMOCA>
+	}
+	...
+}
+``` 
+
+Note that some frames may be missing embeddings due to occlusions or failures in face detection. 
+
+## Dataset Card Authors
+
+Scott Geng
+
+## Dataset Card Contact
+
+sgeng@cs.washington.edu