This repository includes TFDS data loaders for sign language datasets.
pip install git+https://github.com/sign-language-processing/datasets.git
pip install sign-language-datasets
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))
Dataset | Videos | Poses | Versions |
---|---|---|---|
aslg_pc12 | N/A | N/A | 0.0.1 |
rwth_phoenix2014_t | Yes | Holistic | 3.0.0 |
autsl | Yes | OpenPose, Holistic | 1.0.0 |
dgs_corpus | Yes | OpenPose, Holistic | 3.0.0 |
how2sign | Yes | OpenPose | 1.0.0 |
sign2mint | Yes | 1.0.0 | |
signtyp | Links | 1.0.0 | |
swojs_glossario | Yes | 1.0.0 | |
SignBank | N/A | 1.0.0 | |
wlasl | Failed | OpenPose | None |
msasl | None | ||
Video-Based CSL | None | ||
RVL-SLLL ASL | None | ||
ngt_corpus | Yes | 3.0.0 | |
bsl_corpus | No | No | 3.0.0 |
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()
}
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):
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')
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.
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.
@misc{moryossef2021datasets,
title={Sign Language Datasets},
author={Moryossef, Amit},
howpublished={\url{https://github.com/sign-language-processing/datasets}},
year={2021}
}