🖐️GloFE: Gloss-Free End-to-End Sign Language Translation

🌟Accepted at ACL 2023 main conference as Oral. GloFE: Gloss-Free End-to-End Sign Language Translation(arXiv).

Pre-trained Weights

OpenASL

Model Name	BLEU-4	BLEURT	link
GloFE(VN)	7.06	36.35	Google Drive
GloFE(N)	6.82	36.68	Google Drive

How2Sign

Model Name	BLEU-4	BLEURT	link
GloFE(VN)	2.24	31.65	Google Drive

Data Prepreation

File structure for OpenASL dataset

Since OpenASL does not provide pose data, we need to extract using MMPose and organize the extracted features in the following fashion:

/path/to/OpenASL
├── mmpose
│   ├── 00ADU7t7IWI-00:00:00.460-00:00:01.106.pkl
│   ├── xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.pkl
│   ├── ...
└─  └── xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.pkl

File structure for How2Sign dataset

/path/to/How2Sign
├── openpose_output
│   ├── json
│   │   ├── 14BL0rO5e-8_3-8-rgb_front
│   │   │   ├── 14BL0rO5e-8_3-8-rgb_front_000000000000_keypoints.json
│   │   │   ├── 14BL0rO5e-8_3-8-rgb_front_000000000001_keypoints.json
│   │   │   ├── ...
└─  └─  └─  └── 14BL0rO5e-8_3-8-rgb_front_000000000252_keypoints.json

Extracting pose data for OpenASL using MMPose

Set output path in tools/extract_openasl_mp.py:

    # config output_root in tools/extract_openasl_mp.py
    arg_dict = {
        ...
        'output_root': '/path/to/OpenASL/mmpose',
        ...
    }

Generate open_asl_samples.txt and set it in tools/extract_openasl_mp.py. Each line in open_asl_samples.txt is the full path to the mp4 file.

    all_samples = load_sample_names('/path/to/open_asl_samples.txt')

Install MMPose and set the MMPOSE_ROOT(path to MMPose's git dir) in tools/extract.sh. After installation, download the model checkpoints listed in tools/extract.sh and put them in /path/to/mmpose/models. Then run tools/extract.sh

    sh tools/extract.sh 0 1 0
                        │ │ └─ # GPU device-id to run on
                        │ └─── # Number of sub-splits (for parallel extraction)
                        └───── # Split id for this process

If you encountered version mismatch, try the following ones (thanks to @sandeep-sm in issue #4):

MMPose - v0.29.0
mmcv==1.6.2
mmdet==2.25.2
mmtrack>=0.6.0
xtcocotools==1.12

Training and Inference

Environment Setup

Clone the repository:

git clone git@github.com:HenryLittle/GloFE.git

Create conda environment:

conda create --name glofe python=3.8
conda activate glofe
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install -r requirements.txt

For inference or training, use the scripts provided in scripts/{dataset_name}/. All python commands or scripts should be excuted under the GloFE home folder.

Inference

Specifies the following argument to match your local environment before running the script (use OpenASL as an example):

• --work_dir_prefix: path to the work_dir
• --work_dir: supports two level of folders, usually in the format of <dataset>/<exp_name>
• --weights : path to the folder containing downloaded weights and exp_config.json files
• --prefix (Optional): name prefix of the output files
• --tokenizer (Optional): path to tokenizer folder, don't need to modify unless you want to use another one

# scripts/openasl/test.sh
python train_openasl_pose_DDP_inter_VN.py \
    --ngpus 1 \
    --work_dir_prefix "path/to/work_dir" \
    --work_dir "openasl/vn_model" \
    --tokenizer "notebooks/openasl-v1.0/openasl-bpe25000-tokenizer-uncased" \
    --bs 32 \
    --prefix test-vn \
    --phase test --weights "work_dir/openasl/vn_model/glofe_vn_openasl.pt"

Train

For training, we provide DDP script and single card script. Specifies the following argument to match your local environment before running the script (use OpenASL as an example):

• --work_dir_prefix: path to the work_dir
• --work_dir: supports two level of folders, usually in the format of <dataset>/<exp_name>
• --feat_path: path to the extracted pose features
• --label_path: path to the openasl-v1.0.tsv provided by OpenASL
• --tokenizer (Optional): path to tokenizer folder, don't need to modify unless you want to use another one

# scripts/openasl/train_ddp.sh
GPUS=4
torchrun --nnodes=1 --nproc_per_node=$GPUS train_openasl_pose_DDP_inter_VN.py \
    --ngpus $GPUS \
    --work_dir_prefix "path/to/work_dir" \
    --work_dir "openasl/train_test" \
    --bs 48 --ls 0.2 --epochs 400 \
    --save_every 5 \
    --clip_length 512 --vocab_size 25000 \
    --feat_path "path/to/mmpose" \
    --label_path "path/to/openasl-v1.0.tsv" \
    --eos_token "</s>" \
    --tokenizer "notebooks/openasl-v1.0/openasl-bpe25000-tokenizer-uncased" \
    --pose_backbone "PartedPoseBackbone" \
    --pe_enc --mask_enc --lr 3e-4 --dropout_dec 0.1 --dropout_enc 0.1 \
    --inter_cl --inter_cl_margin 0.4 --inter_cl_alpha 1.0 \
    --inter_cl_vocab 5523 \
    --inter_cl_we_path "notebooks/openasl-v1.0/uncased_filtred_glove_VN_embed.pkl"

Miscs

If you plan to run GloFE on other datasets, here are the instructions on how to:

• train the BPE tokenizer
• filter out the anchor words and generate the files needed for the model

*Details are now WIP, refer to the notebooks in the notebooks folder for now.

Citing GloFE

If you find this work useful, please consider giving it a star ⭐ and citing our paper in your work:

@inproceedings{lin-etal-2023-gloss,
    title = "Gloss-Free End-to-End Sign Language Translation",
    author = "Lin, Kezhou  and Wang, Xiaohan  and Zhu, Linchao  and Sun, Ke  and Zhang, Bang  and Yang, Yi",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.722",
    doi = "10.18653/v1/2023.acl-long.722",
    pages = "12904--12916",
}