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"""CVSS speech-to-speech translation corpus.""" |
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import os |
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import datasets |
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_HOMEPAGE = 'https://github.com/google-research-datasets/cvss' |
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_DESCRIPTION = """\ |
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CVSS is a massively multilingual-to-English speech-to-speech translation corpus, |
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covering sentence-level parallel speech-to-speech translation pairs from 21 |
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languages into English. |
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""" |
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_CITATION = """\ |
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@inproceedings{jia2022cvss, |
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title={{CVSS} Corpus and Massively Multilingual Speech-to-Speech Translation}, |
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author={Jia, Ye and Tadmor Ramanovich, Michelle and Wang, Quan and Zen, Heiga}, |
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booktitle={Proceedings of Language Resources and Evaluation Conference (LREC)}, |
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pages={6691--6703}, |
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year={2022} |
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} |
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""" |
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_ROOT_URL = 'https://storage.googleapis.com/cvss' |
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_ALL_LANGUAGES = ('de', 'fr', 'es', 'ca', 'it', 'ru', 'zh', 'pt', 'fa', 'et', |
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'mn', 'nl', 'tr', 'ar', 'sv', 'lv', 'sl', 'ta', 'ja', 'id', |
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'cy') |
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def _get_download_urls(name='cvss_c', languages=_ALL_LANGUAGES, version='1.0'): |
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"""Gets URLs for downloading data. |
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Args: |
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name: 'cvss_c' or 'cvss_t'. |
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languages: An iterable of source languages. |
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version: Only '1.0' available. |
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Returns: |
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A list of URL strs. |
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""" |
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return [ |
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f'{_ROOT_URL}/{name}_v{version}/{name}_{x}_en_v{version}.tar.gz' |
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for x in languages |
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] |
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class CVSSConfig(datasets.BuilderConfig): |
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"""BuilderConfig for CVSS.""" |
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def __init__(self, name, languages='all', **kwargs): |
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"""BuilderConfig for CVSS. |
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Args: |
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name: 'cvss_c' or 'cvss_t'. |
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languages: A list of source languages. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__(name=name, **kwargs) |
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if languages == 'all': |
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self.languages = _ALL_LANGUAGES |
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elif isinstance(languages, str): |
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self.languages = [languages] |
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else: |
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self.languages = languages |
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class CVSS(datasets.GeneratorBasedBuilder): |
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"""CVSS dataset. Version 1.0.""" |
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BUILDER_CONFIG_CLASS = CVSSConfig |
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VERSION = '1.0.0' |
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DEFAULT_WRITER_BATCH_SIZE = 256 |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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features=datasets.Features({ |
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'id': datasets.Value('string'), |
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'file': datasets.Value('string'), |
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'audio': datasets.Audio(sampling_rate=24_000), |
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'text': datasets.Value('string'), |
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})) |
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def _split_generators(self, dl_manager): |
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print(self.config) |
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downloaded_files = dl_manager.download_and_extract( |
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_get_download_urls(self.config.name, self.config.languages)) |
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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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'files': downloaded_files, |
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'split': 'train' |
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}), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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'files': downloaded_files, |
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'split': 'dev' |
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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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'files': downloaded_files, |
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'split': 'test' |
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}), |
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] |
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def _generate_examples(self, files, split): |
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"""Generates examples for each SplitGenerator.""" |
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for path in files: |
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with open(os.path.join(path, f'{split}.tsv'), 'r', encoding='utf-8') as f: |
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for line in f: |
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cols = line.rstrip().split('\t') |
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assert len(cols) == 2, cols |
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key, text = cols |
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audio_path = os.path.join(path, split, f'{key}.wav') |
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yield key, { |
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'id': key, |
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'text': text, |
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'audio': audio_path, |
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'file': audio_path, |
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} |
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