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import os |
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from pandas import read_csv, NA |
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from datasets import GeneratorBasedBuilder, Value, Version, BuilderConfig, Features, DatasetInfo, SplitGenerator, Split, Audio |
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_DESCRIPTION = ''' |
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This dataset contains transcribed sounds emitted by characters of the League of Legends game. |
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The data can be useful for building text classification models, fine-tuning language generation models, speech synthesis and speech recognition models. |
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The underlying web dump for the dataset construction has been last refreshed **20.10.2023** |
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''' |
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_HOMEPAGE = 'https://huggingface.co/datasets/zeio/pale' |
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_LICENSE = 'Apache License Version 2.0' |
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_URLS = { |
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'vanilla': 'https://huggingface.co/datasets/zeio/pale/resolve/main/pale.tsv', |
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'quotes': 'https://huggingface.co/datasets/zeio/pale/resolve/main/quotes.tsv', |
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'annotated': 'https://huggingface.co/datasets/zeio/pale/resolve/main/annotated.tsv', |
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'pulled': 'https://huggingface.co/datasets/zeio/pale/resolve/main/pulled.tsv' |
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} |
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_SOUND_URL = 'https://huggingface.co/datasets/zeio/pale/resolve/main/sound.tar.xz' |
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class Pale(GeneratorBasedBuilder): |
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VERSION = Version('30.10.2023') |
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BUILDER_CONFIGS = [ |
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BuilderConfig(name = 'quotes', version = VERSION, description = 'Truncated version of the corpus, which does\'t contain sound effects'), |
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BuilderConfig(name = 'annotated', version = VERSION, description = 'An extended version of the full configuration with a couple of additional columns with labels'), |
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BuilderConfig(name = 'vanilla', version = VERSION, description = 'All data pulled from the website without significant modifications apart from the web page structure parsing'), |
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BuilderConfig(name = 'pulled', version = VERSION, description = 'Same as vanilla, but sound files have been pulled from the website, and "source" column is replaced with "sound" column') |
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] |
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DEFAULT_CONFIG_NAME = 'quotes' |
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def _info(self): |
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if self.config.name == 'vanilla': |
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features = Features({ |
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'header': Value('string'), |
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'subheader': Value('string'), |
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'text': Value('string'), |
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'source': Value('string'), |
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'champion': Value('string') |
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}) |
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elif self.config.name == 'annotated': |
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features = Features({ |
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'header': Value('string'), |
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'subheader': Value('string'), |
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'text': Value('string'), |
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'source': Value('string'), |
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'champion': Value('string'), |
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'quote': Value('bool') |
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}) |
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elif self.config.name == 'quotes': |
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features = Features({ |
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'header': Value('string'), |
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'subheader': Value('string'), |
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'text': Value('string'), |
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'champion': Value('string') |
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}) |
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elif self.config.name == 'pulled': |
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features = Features({ |
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'header': Value('string'), |
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'subheader': Value('string'), |
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'text': Value('string'), |
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'sound': Audio(sampling_rate = 44_100), |
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'champion': Value('string') |
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}) |
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else: |
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raise ValueError(f'Unknown config: {self.config.name}') |
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return DatasetInfo( |
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description=_DESCRIPTION, |
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features = features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE |
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) |
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def _split_generators(self, dl_manager): |
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name = self.config.name |
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url = _URLS[name] |
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return [ |
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SplitGenerator( |
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name = Split.TRAIN, |
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gen_kwargs = { |
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"path": dl_manager.download_and_extract(url), |
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'sound': dl_manager.download_and_extract(_SOUND_URL) if name == 'pulled' else None |
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} |
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) |
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] |
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def _generate_examples(self, path: str, sound: str): |
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for i, row in read_csv(path, sep = '\t').iterrows(): |
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if sound is None: |
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yield i, dict(row) |
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else: |
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data = dict(row) |
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folder = data['folder'] |
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filename = data['filename'] |
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if folder == folder and filename == filename: |
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data['sound'] = os.path.join(sound, folder, f'{filename}.ogg') |
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else: |
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data['sound'] = NA |
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data.pop('folder') |
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data.pop('filename') |
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yield i, data |
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