---
license: cc-by-nc-2.0
language:
- ar
- fr
- es
- zh
pretty_name: visper
---

# ViSpeR: Multilingual Audio-Visual Speech Recognition

This repository contains **ViSpeR**, a large-scale dataset and models for Visual Speech Recognition for Arabic, Chinese, French, Arabic and Spanish.

## Dataset Summary:

Given the scarcity of publicly available VSR data for non-English languages, we collected VSR data for the most four spoken languages at scale.


Comparison of VSR datasets. Our proposed ViSpeR dataset is larger in size compared to other datasets that cover non-English languages for the VSR task. For our dataset, the numbers in parenthesis denote the number of clips. We also give the clip coverage under TedX and Wild subsets of our ViSpeR dataset.

| Dataset         | French (fr)     | Spanish (es)    | Arabic (ar)     | Chinese (zh)    |
|-----------------|-----------------|-----------------|-----------------|-----------------|
| **MuAVIC**      | 176             | 178             | 16              | --              |
| **VoxCeleb2**   | 124             | 42              | --              | --              |
| **AVSpeech**    | 122             | 270             | --              | --              |
| **ViSpeR (TedX)** | 192 (160k)    | 207 (151k)      | 49 (48k)        | 129 (143k)      |
| **ViSpeR (Wild)** | 680 (481k)    | 587 (383k)      | 1152 (1.01M)    | 658 (593k)      |
| **ViSpeR (full)** | 872 (641k)    | 794 (534k)     | 1200 (1.06M)    | 787 (736k)      |


## Downloading the data:

First, use the provided video lists to download the videos and put them in seperate folders. The raw data should be structured as follows:
```bash
Data/
├── Chinese/
│ ├── video_id.mp4
│ └── ...
├── Arabic/
│ ├── video_id.mp4
│ └── ...
├── French/
│ ├── video_id.mp4
│ └── ...
├── Spanish/
│ ├── video_id.mp4
│ └── ...

```

## Processing the data:

Please refer to our for further details [visper github](https://github.com/YasserdahouML/visper)   

## Intended Use

This dataset can be used to train models for visual speech recognition. It's particularly useful for research and development purposes in the field of audio-visual content processing. The data can be used to assess the performance of current and future models.

## Limitations and Biases
Due to the data collection process focusing on YouTube, biases inherent to the platform may be present in the dataset. Also, while measures are taken to ensure diversity in content, the dataset might still be skewed towards certain types of content due to the filtering process.


## ViSpeR paper coming soon

## Check our VSR related works 
```bash

@inproceedings{djilali2023lip2vec,
  title={Lip2Vec: Efficient and Robust Visual Speech Recognition via Latent-to-Latent Visual to Audio Representation Mapping},
  author={Djilali, Yasser Abdelaziz Dahou and Narayan, Sanath and Boussaid, Haithem and Almazrouei, Ebtessam and Debbah, Merouane},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={13790--13801},
  year={2023}
}

@inproceedings{djilali2024vsr,
  title={Do VSR Models Generalize Beyond LRS3?},
  author={Djilali, Yasser Abdelaziz Dahou and Narayan, Sanath and LeBihan, Eustache and Boussaid, Haithem and Almazrouei, Ebtesam and Debbah, Merouane},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={6635--6644},
  year={2024}
}
```