| Model Name | BLEU-1 | BLEU-2 |
|---|---|---|
| Our Model | 9.48 | 3.15 |
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 YouTubeASL dataset
/path/to/YouTubeASL
├── mmpose
│ ├── 00ADU7t7IWI-00:00:00.460-00:00:01.106.pkl
│ ├── xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.pkl
│ ├── ...
└─ └── xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.pkl
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 encounter a version mismatch, try the following versions:
MMPose - v0.29.0
mmcv==1.6.2
mmdet==2.25.2
mmtrack>=0.6.0
xtcocotools==1.12
Clone the repository:
git clone final_repo_name.git
Create conda environment:
conda create --name grt python=3.8
conda activate grt
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 executed under the GloFE home folder.
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"
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"
Huge thanks to the GloFE paper authors: Lin, K., Wang, X., Zhu, L., Sun, K., Zhang, B., & Yang, Y. (2023). Gloss-Free End-to-End Sign Language Translation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 12904–12916). Association for Computational Linguistics.