CNVSRC 2023 Baseline

Introduction

This repository is the baseline code for CNVSRC2023 (Chinese Continuous Visual Speech Recognition Challenge 2023).

The code in this repository is based on the SOTA method mpc001/auto_avsr on the LRS3 dataset. We have added some configuration files to run this code on CN-CVS and the datasets provided in this challenge. Additionally, we have removed some code that is not needed for running this baseline and modified the implementation of some functionalities.

Preparation

1. Clone the repository and navigate to it:

git clone git@github.com:MKT-Dataoceanai/CNVSRC2023Baseline.git
cd CNVSRC2023Baseline
git submodule init
git submodule update
cd tools/face_detection
git lfs pull
cd ../../

If you don't have git-lfs installed, Please go to here to download the model file and put it into CNVSRC2023Baseline/tools/face_detection/ibug/face_detection/retina_face/weights/

2. Set up the environment:

# create environment
conda create -y -n cnvsrc python==3.10.11
conda activate cnvsrc
# install pytorch torchaudio torchvision
conda install pytorch-lightning==1.9.3 pytorch==2.0.1 torchaudio==2.0.2 torchvision==0.15.2 torchmetrics==0.11.2 pytorch-cuda==11.8 cudatoolkit==11.8 -c pytorch -c nvidia -y
# install fairseq
cd tools/fairseq/
pip install --editable .
# install face_alignment
cd ../face_alignment/
pip install --editable .
# install face_detection
cd ../face_detection/
pip install --editable .
# install other dependence
cd ../../
pip install -r reqs.txt

3. Download and preprocess the dataset. See the instructions in the preparation folder.

4. Download the models into path pretrained_models/

Logging

We use tensorboard as the logger.

Tensorboard logging files will be writen to outputs/$DATE/$TIME/tblog/

Training

Please modify the specified yaml configuration file in main.py to select the training configuration.

Configuration File

The conf folder lists the configuration files required for this baseline.

Before running any training or testing, please make sure to modify the code_root_dir and data_root_dir in the corresponding yaml file to the path of this repository and the path where the dataset is located, respectively.

data.dataset specifies the path of the dataset and the path of the .csv files.

Taking cncvs as an example:

1. After data preprocessing is complete, please set ${data_root_dir} to the parent directory of cncvs/ and copy data/cncvs/*.csv to ${data_root_dir}/cncvs/*.csv.

2. At this point, the folder structure of ${data_root_dir} is as follows:

   ${data_root_dir}
   └── cncvs
      ├── test.csv
      ├── train.csv
      ├── valid.csv
      ├── news
      |   ├── n001
      |   |   └── video
      |   |       ├── ...
      |   |       └── n001_00001_001.mp4
      |   ├── n002
      |   ├── ...
      |   └── n028
      └── speech
          ├── s00001
          ├── s00002
          ├── ...
          └── s02529

3. The contents of *.csv at this point are as follows:

   cncvs,news/n001/video/n001_00001_001.mp4,x,x x x x x x x x x x x

   Each line contains the following content:

   ${dataset_name},${video_filename},${frame_count},${token_idx_split_by_blank}

Training from a pre-trained model

The configuration file train_cnvsrc-multi.yaml provides an example configuration for fine-tuning a model pretrained on CN-CVS.

The ckpt_path in the configuration file specifies the path of the pretrained model. The remove_ctc option indicates whether to use the classification layer of the pretrained model.

python main.py

Training from scratch through curriculum learning

[Stage 1] Train the model using the subset that includes only short utterances lasting no more than 4 seconds (100 frames). We set optimizer.lr to 0.0002 at the first stage.

By setting data.max_length=100 in config files to ensure training use only short utterances.

See train_cncvs_4s.yaml for more information.

[Stage 2] Use the checkpoint from stage 1 to initialise the model and train the model with the full dataset.

See train_cncvs_4s_30s.yaml for more information.

Testing

You can use either main.py or predict.py to run testing as soon as you choose the proper config filename in the python file.

We prefer using predict.py to observe the real-time output text.

Model zoo

The table below contains the CER performance of Models on their own task.

Download model files from huggingface or modelscope。

License

It is noted that the code can only be used for comparative or benchmarking purposes. Users can only use code supplied under a License for non-commercial purposes.

Contact

[Chen Chen](chenchen[at]cslt.org)
[CNVSRC2023](cnvsrc[at]cnceleb.org)