Datasets:
Tasks:
Translation
Multilinguality:
translation
Size Categories:
n<1K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
License:
# coding=utf-8 | |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
"""NCSLGR: a small American Sign Language corpus annotated with non-manual features""" | |
import os | |
import re | |
from dataclasses import dataclass | |
import datasets | |
_DESCRIPTION = """ | |
A small corpus of American Sign Language (ASL) video data from native signers, annotated with non-manual features. | |
""" | |
_CITATION = """\ | |
@misc{dataset:databases2007volumes, | |
title={Volumes 2--7}, | |
author={Databases, NCSLGR}, | |
year={2007}, | |
publisher={American Sign Language Linguistic Research Project (Distributed on CD-ROM~…} | |
} | |
""" | |
_URL_ANNOTATIONS = "http://asl.cs.depaul.edu/corpus/elanBUcorpus.zip" | |
_URL_VIDEOS = "http://asl.cs.depaul.edu/corpus/video.zip" | |
_HOMEPAGE = "https://www.bu.edu/asllrp/ncslgr.html" | |
class NCSLGRConfig(datasets.BuilderConfig): | |
"""BuilderConfig for NCSLGR.""" | |
videos: bool = True | |
class NCSLGR(datasets.GeneratorBasedBuilder): | |
"""NCSLGR: a small American Sign Language corpus annotated with non-manual features""" | |
VERSION = datasets.Version("0.7.0") | |
BUILDER_CONFIG_CLASS = NCSLGRConfig | |
BUILDER_CONFIGS = [ | |
NCSLGRConfig( | |
name="entire_dataset", | |
version=datasets.Version("0.7.0"), | |
description="Entire dataset containing both videos and annotations.", | |
videos=True, | |
), | |
NCSLGRConfig( | |
name="annotations", | |
version=datasets.Version("0.7.0"), | |
description="Dataset including only annotations, without videos", | |
videos=False, | |
), | |
] | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
# This defines the different columns of the dataset and their types | |
features=datasets.Features( | |
{ | |
"eaf": datasets.Value("string"), # EAF path | |
"sentences": datasets.features.Sequence( | |
{ | |
"gloss": datasets.Value("string"), # ASL Gloss | |
"text": datasets.Value("string"), # English Text | |
} | |
), | |
"videos": datasets.features.Sequence(datasets.Value("string")), # Videos paths | |
} | |
), | |
homepage=_HOMEPAGE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
eaf_path = os.path.join(dl_manager.download_and_extract(_URL_ANNOTATIONS), "elanBUcorpus") | |
videos_path = dl_manager.download_and_extract(_URL_VIDEOS) if self.config.videos else None | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={"eaf_path": eaf_path, "videos_path": videos_path}, | |
) | |
] | |
def _extract_sentences(self, elan_xml: str): | |
def get_tier_values(name: str): | |
tiers = re.findall('TIER_ID="' + name + '">([\\s\\S]*?)</TIER>', elan_xml) | |
if len(tiers) == 0: | |
return [] | |
tier = tiers[0] | |
return [ | |
(int(s[2:]), int(e[2:]), t) | |
for s, e, t in re.findall( | |
'TIME_SLOT_REF1="(.*)" TIME_SLOT_REF2="(.*)">\n.*<ANNOTATION_VALUE>(.*?)</ANNOTATION_VALUE>', tier | |
) | |
] | |
gloss = get_tier_values("main gloss") | |
texts = get_tier_values("English translation") | |
for s, e, text in texts: | |
relevant_gloss = [t for (s2, e2, t) in gloss if s2 >= s and e2 <= e] | |
yield {"gloss": " ".join(relevant_gloss), "text": text} | |
def _generate_examples(self, eaf_path: str, videos_path: str): | |
"""Yields examples.""" | |
for i, eaf_file in enumerate(os.listdir(eaf_path)): | |
eaf_file_path = os.path.join(eaf_path, eaf_file) | |
videos = [] | |
with open(eaf_file_path, "r", encoding="utf-8") as f: | |
content = f.read() | |
if self.config.videos: | |
videos_relative = re.findall('RELATIVE_MEDIA_URL="(.*)"', content) | |
videos = [os.path.join(videos_path, v[3:]) for v in videos_relative] | |
sentences = list(self._extract_sentences(content)) | |
yield i, {"eaf": eaf_file_path, "videos": videos, "sentences": sentences} | |