Sign Language Datasets

This repository includes TFDS data loaders for sign language datasets.

Installation

From Source

pip install git+https://github.com/sign-language-processing/datasets.git

PyPi

pip install sign-language-datasets

For apple environments, you may need touse an ARM version of conda:

# Uninstall Anaconda
conda activate base
conda install anaconda-clean
anaconda-clean --yes
conda deactivate
brew uninstall anaconda

# Download and install Miniforge for ARM (Apple Silicon)
curl -L -O https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh
bash Miniforge3-MacOSX-arm64.sh
# Follow the instructions

conda create --name=datasets python=3.11
conda activate datasets
pip install .

# Test the installation
python -c "import tensorflow"
python -c "import sign_language_datasets"

Usage

We demonstrate a loading script for every dataset in examples/load.ipynb

Our config includes the option to choose the resolution and fps, for example:

import tensorflow_datasets as tfds
import sign_language_datasets.datasets
from sign_language_datasets.datasets.config import SignDatasetConfig

# Loading a dataset with default configuration
aslg_pc12 = tfds.load("aslg_pc12")

# Loading a dataset with custom configuration
config = SignDatasetConfig(name="videos_and_poses256x256:12",
                           version="3.0.0",  # Specific version
                           include_video=True,  # Download and load dataset videos
                           process_video=True,  # Process videos to tensors, or only save path to video
                           fps=12,  # Load videos at constant, 12 fps
                           resolution=(256, 256),  # Convert videos to a constant resolution, 256x256
                           include_pose="holistic")  # Download and load Holistic pose estimation
rwth_phoenix2014_t = tfds.load(name='rwth_phoenix2014_t', builder_kwargs=dict(config=config))

Datasets

Data Interface

We follow the following interface wherever possible to make it easy to swap datasets.

{
    "id": tfds.features.Text(),
    "signer": tfds.features.Text() | tf.int32,
    "video": tfds.features.Video(shape=(None, HEIGHT, WIDTH, 3)),
    "depth_video": tfds.features.Video(shape=(None, HEIGHT, WIDTH, 1)),
    "fps": tf.int32,
    "pose": {
        "data": tfds.features.Tensor(shape=(None, 1, POINTS, CHANNELS), dtype=tf.float32),
        "conf": tfds.features.Tensor(shape=(None, 1, POINTS), dtype=tf.float32)
    },
    "gloss": tfds.features.Text(),
    "text": tfds.features.Text()
}

Adding a new dataset

For general instructions, see the TFDS guide to writing custom datasets. Instructions below are specific to this repository.

Make a new folder inside sign_language_datasets/datasets with the same name as the dataset. As a convention, the name of the dataset should be lowercase and words should be separated by an underscore. Example:

cd sign_language_datasets/datasets
tfds new new_dataset

For our purposes, creating a custom TFDS dataset means writing a new class which inherits from tfds.core.GeneratorBasedBuilder. If you use tfds new to create a new dataset then the dataset class is stored in a file with the exact same name as the dataset, i.e. new_dataset.py. new_dataset.py must contain a line similar to:

class NewDataset(tfds.core.GeneratorBasedBuilder):

Registering a new dataset

The mechanism to add a custom dataset to TFDS' dataset registry is to import the class NewDataset. For this reason the folder sign_language_datasets/datasets/new_dataset must have an __init__.py file that imports the class NewDataset:

from .new_dataset import NewDataset

Even though the name of the class is NewDataset, it will be available for loading in lowercase and uppercase characters are interpreted as the start of a new word that should be separated with an underscore. This means that the class can be loaded as follows:

ds = tfds.load('new_dataset')

Generating checksums

The folder for the new dataset should contain a file checksums.tsv with checksums for every file in the dataset. This allows the TFDS download manager to check the integrity of the data it downloads. Use the tfds build tool to generate the checksum file:

tfds build --register_checksums new_dataset.py

Use a dataset configuration which includes all files (e.g. does include the video files if any) using the --config argument. The default behaviour is to build all configurations which might be redundant.

Why not Huggingface Datasets?

Huggingface datasets do not work well with videos. From the lack of native support of the video type, to lack of support of arbitrary tensors. Furthermore, they currently have memory leaks that prevent from saving even the smallest of video datasets.

Cite

@misc{moryossef2021datasets, 
    title={Sign Language Datasets},
    author={Moryossef, Amit and M\"{u}ller, Mathias},
    howpublished={\url{https://github.com/sign-language-processing/datasets}},
    year={2021}
}