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"""Rakeffet dataset.""" |
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import os |
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import datasets |
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from datasets import load_dataset |
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from datasets.tasks import AutomaticSpeechRecognition |
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_CITATION = """\ |
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@inproceedings{Zandie2021RakeffetAC, |
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title={Rakeffet AI}, |
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author={Yisroel Lazerson}, |
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booktitle={Cooolio}, |
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year={2022} |
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} |
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""" |
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_DESCRIPTION = "Rakeffet is cool." |
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_URL = "google.com" |
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_NAME = "rakeffet" |
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_DL_URLS = { |
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"dev": "https://huggingface.co/datasets/izzy-lazerson/rakeffet/resolve/main/data/dev.tar.gz", |
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"test": "https://huggingface.co/datasets/izzy-lazerson/rakeffet/resolve/main/data/test.tar.gz", |
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"train": "https://huggingface.co/datasets/izzy-lazerson/rakeffet/resolve/main/data/train.tar.gz" |
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} |
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class RakeffetConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Rakeffet.""" |
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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( |
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RakeffetConfig, |
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self |
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).__init__( |
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version=datasets.Version("1.0.0", ""), |
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**kwargs |
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) |
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class Rakeffet(datasets.GeneratorBasedBuilder): |
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"""Rakeffet dataset.""" |
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BUILDER_CONFIGS = [ |
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RakeffetConfig( |
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name=_NAME, |
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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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"id": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=16_000), |
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"text": datasets.Value("string"), |
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} |
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), |
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supervised_keys=("id", "text"), |
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homepage=_URL, |
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citation=_CITATION, |
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task_templates=[ |
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AutomaticSpeechRecognition( |
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audio_column="audio", |
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transcription_column="text" |
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) |
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], |
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) |
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def _split_generators(self, dl_manager): |
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archive_path = dl_manager.download(_DL_URLS) |
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local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {} |
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train_splits = [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive.get("train"), |
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"files": dl_manager.iter_archive(archive_path["train"]), |
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}, |
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) |
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] |
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dev_splits = [ |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive.get("dev"), |
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"files": dl_manager.iter_archive(archive_path["dev"]), |
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}, |
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) |
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] |
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test_splits = [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive.get("test"), |
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"files": dl_manager.iter_archive(archive_path["test"]), |
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}, |
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) |
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] |
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return train_splits + dev_splits + test_splits |
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def _generate_examples(self, files, local_extracted_archive): |
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"""Generate examples from a Rakeffet archive_path.""" |
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audio_data = {} |
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transcripts = {} |
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paths = {} |
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for path, f in files: |
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if path.endswith(".mp3"): |
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id_ = path.split("/")[-1][: -len(".mp3")] |
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audio_data[id_] = f.read() |
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paths[id_] = os.path.join(local_extracted_archive, path) |
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elif path.endswith(".csv"): |
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for line in f: |
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line_fields = line.decode("utf-8").split(',') |
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id_ = line_fields[0] |
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transcripts[id_] = line_fields[1].strip() |
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for key, id_ in enumerate(transcripts): |
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yield key, {"audio": {"bytes": audio_data[id_], |
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"path": paths[id_]}, |
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"text": transcripts[id_], |
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"id": id_} |
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