Datasets:
ncslgr

	# 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.
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	# http://www.apache.org/licenses/LICENSE-2.0
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	"""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}