Datasets:
Tasks:
Translation
Multilinguality:
translation
Size Categories:
n<1K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
File size: 4,964 Bytes
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# 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"
@dataclass
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}
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