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import os |
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import string |
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import datasets |
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from datasets.tasks import AutomaticSpeechRecognition |
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_DATA_URL = "https://www.openslr.org/resources/52/asr_sinhala_{}.zip" |
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_CITATION = """\ |
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@inproceedings{kjartansson-etal-sltu2018, |
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title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese, Sinhala, Nepali, and Bangladeshi Bengali}}, |
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author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha}, |
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booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)}, |
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year = {2018}, |
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address = {Gurugram, India}, |
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month = aug, |
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pages = {52--55}, |
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URL = {http://dx.doi.org/10.21437/SLTU.2018-11} |
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} |
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""" |
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_DESCRIPTION = """\ |
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This data set contains ~185K transcribed audio data for Sinhala. The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, anonymized UserID and the transcription of audio in the file. |
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The data set has been manually quality checked, but there might still be errors. |
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See LICENSE.txt file for license information. |
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Copyright 2016, 2017, 2018 Google, Inc. |
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""" |
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_HOMEPAGE = "https://www.openslr.org/52/" |
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_LICENSE = "https://www.openslr.org/resources/52/LICENSE" |
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_LANGUAGES = { |
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"si": { |
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"Language": "Sinhala", |
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"Date": "2018", |
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}, |
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} |
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class LargeASRConfig(datasets.BuilderConfig): |
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"""BuilderConfig for LargeASR.""" |
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def __init__(self, name, **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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self.language = kwargs.pop("language", None) |
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self.date_of_snapshot = kwargs.pop("date", None) |
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description = f"Large Sinhala dataset in {self.language} of {self.date_of_snapshot}." |
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super(LargeASRConfig, self).__init__( |
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name=name, version=datasets.Version("1.0.0", ""), description=description, **kwargs |
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) |
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class LargeASR(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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LargeASRConfig( |
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name=lang_id, |
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language=_LANGUAGES[lang_id]["Language"], |
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date=_LANGUAGES[lang_id]["Date"], |
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) |
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for lang_id in _LANGUAGES.keys() |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"filename": datasets.Value("string"), |
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"x": datasets.Value("string"), |
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"sentence": datasets.Value("string"), |
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"file": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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task_templates=[ |
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AutomaticSpeechRecognition(audio_file_path_column="file", transcription_column="sentence") |
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], |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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data_urls = [_DATA_URL.format(i) for i in (string.digits + string.ascii_lowercase[:6])] |
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dl_path = dl_manager.download_and_extract(data_urls) |
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print(dl_path) |
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abs_path_to_data = os.path.join('./') |
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abs_path_to_clips = os.path.join(dl_path) |
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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={ |
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"filepath": os.path.join(abs_path_to_data, "train.tsv"), |
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"path_to_clips": abs_path_to_clips, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": os.path.join(abs_path_to_data, "test.tsv"), |
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"path_to_clips": abs_path_to_clips, |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, path_to_clips): |
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"""Yields examples.""" |
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data_fields = list(self._info().features.keys()) |
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path_idx = data_fields.index("file") |
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with open(filepath, encoding="utf-8") as f: |
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lines = f.readlines() |
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headline = lines[0] |
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column_names = headline.strip().split("\t") |
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assert ( |
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column_names == data_fields |
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), f"The file should have {data_fields} as column names, but has {column_names}" |
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for id_, line in enumerate(lines[1:]): |
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field_values = line.strip().split("\t") |
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field_values[path_idx] = os.path.join(path_to_clips, field_values[path_idx]) |
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if len(field_values) < len(data_fields): |
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field_values += (len(data_fields) - len(field_values)) * ["''"] |
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yield id_, {key: value for key, value in zip(data_fields, field_values)} |
