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"""NCSLGR: a small American Sign Language corpus annotated with non-manual features""" |
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import os |
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import re |
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from dataclasses import dataclass |
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import datasets |
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_DESCRIPTION = """ |
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A small corpus of American Sign Language (ASL) video data from native signers, annotated with non-manual features. |
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""" |
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_CITATION = """\ |
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@misc{dataset:databases2007volumes, |
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title={Volumes 2--7}, |
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author={Databases, NCSLGR}, |
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year={2007}, |
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publisher={American Sign Language Linguistic Research Project (Distributed on CD-ROM~…} |
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} |
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""" |
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_URL_ANNOTATIONS = "http://asl.cs.depaul.edu/corpus/elanBUcorpus.zip" |
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_URL_VIDEOS = "http://asl.cs.depaul.edu/corpus/video.zip" |
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_HOMEPAGE = "https://www.bu.edu/asllrp/ncslgr.html" |
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@dataclass |
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class NCSLGRConfig(datasets.BuilderConfig): |
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"""BuilderConfig for NCSLGR.""" |
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videos: bool = True |
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class NCSLGR(datasets.GeneratorBasedBuilder): |
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"""NCSLGR: a small American Sign Language corpus annotated with non-manual features""" |
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VERSION = datasets.Version("0.7.0") |
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BUILDER_CONFIG_CLASS = NCSLGRConfig |
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BUILDER_CONFIGS = [ |
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NCSLGRConfig( |
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name="entire_dataset", |
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version=datasets.Version("0.7.0"), |
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description="Entire dataset containing both videos and annotations.", |
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videos=True, |
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), |
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NCSLGRConfig( |
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name="annotations", |
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version=datasets.Version("0.7.0"), |
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description="Dataset including only annotations, without videos", |
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videos=False, |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"eaf": datasets.Value("string"), |
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"sentences": datasets.features.Sequence( |
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{ |
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"gloss": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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} |
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), |
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"videos": datasets.features.Sequence(datasets.Value("string")), |
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} |
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), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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eaf_path = os.path.join(dl_manager.download_and_extract(_URL_ANNOTATIONS), "elanBUcorpus") |
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videos_path = dl_manager.download_and_extract(_URL_VIDEOS) if self.config.videos else None |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"eaf_path": eaf_path, "videos_path": videos_path}, |
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) |
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] |
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def _extract_sentences(self, elan_xml: str): |
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def get_tier_values(name: str): |
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tiers = re.findall('TIER_ID="' + name + '">([\\s\\S]*?)</TIER>', elan_xml) |
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if len(tiers) == 0: |
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return [] |
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tier = tiers[0] |
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return [ |
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(int(s[2:]), int(e[2:]), t) |
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for s, e, t in re.findall( |
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'TIME_SLOT_REF1="(.*)" TIME_SLOT_REF2="(.*)">\n.*<ANNOTATION_VALUE>(.*?)</ANNOTATION_VALUE>', tier |
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) |
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] |
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gloss = get_tier_values("main gloss") |
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texts = get_tier_values("English translation") |
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for s, e, text in texts: |
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relevant_gloss = [t for (s2, e2, t) in gloss if s2 >= s and e2 <= e] |
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yield {"gloss": " ".join(relevant_gloss), "text": text} |
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def _generate_examples(self, eaf_path: str, videos_path: str): |
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"""Yields examples.""" |
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for i, eaf_file in enumerate(os.listdir(eaf_path)): |
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eaf_file_path = os.path.join(eaf_path, eaf_file) |
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videos = [] |
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with open(eaf_file_path, "r", encoding="utf-8") as f: |
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content = f.read() |
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if self.config.videos: |
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videos_relative = re.findall('RELATIVE_MEDIA_URL="(.*)"', content) |
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videos = [os.path.join(videos_path, v[3:]) for v in videos_relative] |
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sentences = list(self._extract_sentences(content)) |
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yield i, {"eaf": eaf_file_path, "videos": videos, "sentences": sentences} |
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