# MultiTalk (INTERSPEECH 2024)

### [Project Page](https://multi-talk.github.io/) | [Paper](https://arxiv.org/abs/2406.14272) | [Dataset](https://github.com/postech-ami/MultiTalk/blob/main/MultiTalk_dataset/README.md)
This repository contains a pytorch implementation for the Interspeech 2024 paper, [MultiTalk: Enhancing 3D Talking Head Generation Across Languages with Multilingual Video Dataset](https://multi-talk.github.io/). MultiTalk generates 3D talking head with enhanced multilingual performance.<br><br>

<img width="700" alt="teaser" src="./assets/teaser.png"> 

## Getting started
This code was developed on Ubuntu 18.04 with Python 3.8, CUDA 11.3 and PyTorch 1.12.0. Later versions should work, but have not been tested.

### Installation
Create and activate a virtual environment to work in:
```
conda create --name multitalk python=3.8
conda activate multitalk
```

Install [PyTorch](https://pytorch.org/). For CUDA 11.3 and ffmpeg, this would look like:
```
pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113
conda install -c conda-forge ffmpeg
```

Install the remaining requirements with pip:
```
pip install -r requirements.txt 
```

Compile and install `psbody-mesh` package:
[MPI-IS/mesh](https://github.com/MPI-IS/mesh)
```
BOOST_INCLUDE_DIRS=/usr/lib/x86_64-linux-gnu make all
```


### Download models
To run MultiTalk, you need to download stage1 and stage2 model, and the template file of mean face in FLAME topology,
Download [stage1 model](https://drive.google.com/file/d/1jI9feFcUuhXst1pM1_xOMvqE8cgUzP_t/view?usp=sharing | [stage2 model](https://drive.google.com/file/d/1zqhzfF-vO_h_0EpkmBS7nO36TRNV4BCr/view?usp=sharing) | [template](https://drive.google.com/file/d/1WuZ87kljz6EK1bAzEKSyBsZ9IlUmiI-i/view?usp=sharing) and download FLAME_sample.ply from [voca](https://github.com/TimoBolkart/voca/tree/master/template).

After downloading the models, place them in `./checkpoints`.
```
./checkpoints/stage1.pth.tar
./checkpoints/stage2.pth.tar
./checkpoints/FLAME_sample.ply
```

## Demo
Run below command to train the model.
We provide sample audios in **./demo/input**.
```
sh scripts/demo.sh multi
```

To use wav2vec of `facebook/wav2vec2-large-xlsr-53`, please move to `/path/to/conda_environment/lib/python3.8/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py` and change the code as below.
```
L105: tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
to
L105: tokenizer=Wav2Vec2CTCTokenizer.from_pretrained("facebook/wav2vec2-base-960h",**kwargs)
```

## MultiTalk Dataset
Please follow the instructions in [MultiTalk_dataset/README.md](https://github.com/postech-ami/MultiTalk/blob/main/MultiTalk_dataset/README.md).

## Training and testing

### Training for Discrete Motion Prior
```
sh scripts/train_multi.sh MultiTalk_s1 config/multi/stage1.yaml multi s1
```

### Training for Speech-Driven Motion Synthesis
Make sure the paths of pre-trained models are correct, i.e.,`vqvae_pretrained_path` and `wav2vec2model_path` in `config/multi/stage2.yaml`.
```
sh scripts/train_multi.sh MultiTalk_s2 config/multi/stage2.yaml multi s2
```

### Testing
#### Lip Vertex Error (LVE)
For evaluating the lip vertex error, please run below command.

```
sh scripts/test.sh MultiTalk_s2 config/multi/stage2.yaml vocaset s2
```

#### Audio-Visual Lip Reading (AVLR)
For evaluating lip readability with a pre-trained Audio-Visual Speech Recognition (AVSR), download language specific checkpoint, dictionary, and tokenizer from [muavic](https://github.com/facebookresearch/muavic).   
Place them in `./avlr/${language}/checkpoints/${language}_avlr`.
```
# e.g "Arabic" 
./avlr/ar/checkpoints/ar_avsr/checkpoint_best.pt
./avlr/ar/checkpoints/ar_avsr/dict.ar.txt
./avlr/ar/checkpoints/ar_avsr/tokenizer.model
```
And place the rendered videos in `./avlr/${language}/inputs/MultiTalk`, corresponding wav files in `./avlr/${language}/inputs/wav`.
```
# e.g "Arabic" 
./avlr/ar/inputs/MultiTalk
./avlr/ar/inputs/wav
```

Run below command to evaluate lip readability.
```
python eval_avlr/eval_avlr.py --avhubert-path ./av_hubert/avhubert --work-dir ./avlr --language ${language} --model-name MultiTalk --exp-name ${exp_name}
```



[//]: # (## **Citation**)

[//]: # ()
[//]: # (If you find the code useful for your work, please star this repo and consider citing:)

[//]: # ()
[//]: # (```)

[//]: # (@inproceedings{xing2023codetalker,)

[//]: # (  title={Codetalker: Speech-driven 3d facial animation with discrete motion prior},)

[//]: # (  author={Xing, Jinbo and Xia, Menghan and Zhang, Yuechen and Cun, Xiaodong and Wang, Jue and Wong, Tien-Tsin},)

[//]: # (  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},)

[//]: # (  pages={12780--12790},)

[//]: # (  year={2023})

[//]: # (})

[//]: # (```)

## **Notes**
1. Although our codebase allows for training with multi-GPUs, we did not test it and just hardcode the training batch size as one. You may need to change the `data_loader` if needed.


## **Acknowledgement**
We heavily borrow the code from
[Codetalk](https://doubiiu.github.io/projects/codetalker/).
We sincerely appreciate those authors.