README.md

---
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}  
}
```

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## 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