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"""Librispeech automatic speech recognition dataset.""" |
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from __future__ import absolute_import, division, print_function |
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import glob |
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import os |
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import datasets |
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_CITATION = """\ |
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""" |
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_DESCRIPTION = """\ |
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This is Zeroth-Korean corpus, |
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licensed under Attribution 4.0 International (CC BY 4.0) |
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The data set contains transcriebed audio data for Korean. There are 51.6 hours transcribed Korean audio for training data (22,263 utterances, 105 people, 3000 sentences) and 1.2 hours transcribed Korean audio for testing data (457 utterances, 10 people). This corpus also contains pre-trained/designed language model, lexicon and morpheme-based segmenter(morfessor). |
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Zeroth project introduces free Korean speech corpus and aims to make Korean speech recognition more broadly accessible to everyone. |
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This project was developed in collaboration between Lucas Jo(@Atlas Guide Inc.) and Wonkyum Lee(@Gridspace Inc.). |
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Contact: Lucas Jo(lucasjo@goodatlas.com), Wonkyum Lee(wonkyum@gridspace.com) |
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""" |
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_URL = "http://www.openslr.org/40" |
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_DL_URL = "https://www.openslr.org/resources/40/zeroth_korean.tar.gz" |
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class ZerothKoreanASRConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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""" |
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Args: |
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data_dir: `string`, the path to the folder containing the files in the |
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downloaded .tar |
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citation: `string`, citation for the data set |
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url: `string`, url for information about the data set |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(ZerothKoreanASRConfig, self).__init__(version=datasets.Version("1.0.1", ""), **kwargs) |
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class ZerothKoreanASR(datasets.GeneratorBasedBuilder): |
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"""Librispeech dataset.""" |
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BUILDER_CONFIGS = [ |
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ZerothKoreanASRConfig(name="clean", description="'Clean' speech.") |
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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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"file": datasets.Value("string"), |
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"audio": datasets.features.Audio(sampling_rate=16_000), |
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"text": datasets.Value("string"), |
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"speaker_id": datasets.Value("int64"), |
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"chapter_id": datasets.Value("int64"), |
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"id": datasets.Value("string"), |
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} |
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), |
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supervised_keys=("speech", "text"), |
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homepage=_URL, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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archive_path = dl_manager.download_and_extract(_DL_URL) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"archive_path": archive_path, "split_name": f"train_data_01"}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split_name": f"test_data_01"}), |
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] |
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def _generate_examples(self, archive_path, split_name): |
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transcripts_glob = os.path.join(archive_path, split_name, "*/*/*.txt") |
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for transcript_file in glob.glob(transcripts_glob): |
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path = os.path.dirname(transcript_file) |
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with open(os.path.join(path, transcript_file), encoding="utf-8-sig") as f: |
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for line in f: |
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line = line.strip() |
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key, transcript = line.split(" ", 1) |
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audio_file = f"{key}.flac" |
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speaker_id, chapter_id = [int(el) for el in key.split("_")[:2]] |
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example = { |
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"id": key, |
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"speaker_id": speaker_id, |
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"chapter_id": chapter_id, |
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"file": os.path.join(path, audio_file), |
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"audio": os.path.join(path, audio_file), |
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"text": transcript, |
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} |
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yield key, example |
