Leveraging-Self-Supervised-Learning-for-AVSR

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Leveraging-Self-Supervised-Learning-for-AVSR

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Official PyTorch implementation of paper Leveraging Unimodal Self Supervised Learning for Multimodal Audio-Visual Speech Recognition (ACL 2022)

Leveraging-Self-Supervised-Learning-for-AVSR 최신버젼 다운로드

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# Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition
This is the official PyTorch implementation of paper [Leveraging Unimodal Self-supervised Learning for Multimodal Audio-visual Speech Recognition](https://arxiv.org/abs/2203.07996)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/leveraging-uni-modal-self-supervised-learning-1/audio-visual-speech-recognition-on-lrs2)](https://paperswithcode.com/sota/audio-visual-speech-recognition-on-lrs2?p=leveraging-uni-modal-self-supervised-learning-1)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/leveraging-uni-modal-self-supervised-learning-1/automatic-speech-recognition-on-lrs2)](https://paperswithcode.com/sota/automatic-speech-recognition-on-lrs2?p=leveraging-uni-modal-self-supervised-learning-1)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/leveraging-uni-modal-self-supervised-learning-1/lipreading-on-lrs2)](https://paperswithcode.com/sota/lipreading-on-lrs2?p=leveraging-uni-modal-self-supervised-learning-1)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/leveraging-uni-modal-self-supervised-learning-1/lipreading-on-lip-reading-in-the-wild)](https://paperswithcode.com/sota/lipreading-on-lip-reading-in-the-wild?p=leveraging-uni-modal-self-supervised-learning-1)

## Install the environment
1. Clone the repo into a directory. 
```shell
git clone https://github.com/LUMIA-Group/Leveraging-Self-Supervised-Learning-for-AVSR.git
```
2. Install all required packages.
```shell
pip install -r requirements.txt
```
Noted that the Pytorch-Lightning lib do not support a wrapped ReduceLROnPlateau scheduler, we need to modify the lib manually by:
```shell
python -c "exec(\"import pytorch_lightning\nprint(pytorch_lightning.__file__)\")"
vi /path/to/pytorch_lightning/trainer/optimizers.py
```
and comments the 154-156 lines
```python
# scheduler["reduce_on_plateau"] = isinstance(
#     scheduler["scheduler"], optim.lr_scheduler.ReduceLROnPlateau
# )
```

## Preprocess the dataset
1. Download [LRW dataset](https://www.robots.ox.ac.uk/~vgg/data/lip_reading/lrw1.html) and [LRS2 dataset](https://www.robots.ox.ac.uk/~vgg/data/lip_reading/lrs2.html)
2. Download pretrained [MoCo v2 model](https://dl.fbaipublicfiles.com/moco/moco_checkpoints/moco_v2_800ep/moco_v2_800ep_pretrain.pth.tar) and [wav2vec 2.0 model](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_new.pt)
3. Change the directory in `config.py` to "relative directory" relative to the project root directory
4. Preprocessing the LRW dataset.
```shell
cd trainFrontend
python saveh5.py
```
5. Preprocessing the LRS2 dataset.
```shell
python saveh5.py
```

## Training
1. Train the visual front-end on LRW.
```shell
python trainfrontend.py
```
2. Change the `args["MOCO_FRONTEND_FILE"]` in `config.py` to the trained front-end file, and config `args["MODAL"]` to choose modality.
3. Train the AO and VO model first.
```shell
python train.py
```
4. Then train the AV model. Before that, change the `args["TRAINED_AO_FILE"]` and `args["TRAINED_VO_FILE"]` to the trained AO and VO model.
```shell
python train.py
```

## Evaluation
1. Choose test configuration and model.
2. Evaluate the visual word classification performance.
```shell
python evalfrontend.py
```
3. Evaluate the AO/VO/AV model.
```shell
python eval.py
```

## Cite
If you find this repo useful in your research, please consider citing it in the following format:
```
@inproceedings{pan2022leveraging,
  title={Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition},
  author={Pan, Xichen and Chen, Peiyu and Gong, Yichen and Zhou, Helong and Wang, Xinbing and Lin, Zhouhan},
  booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={4491--4503},
  year={2022}
}
```

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