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"""CrowdSpeech: Benchmark Dataset for Crowdsourced Audio Transcription.""" |
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import json |
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import datasets |
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from datasets.tasks import Summarization |
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logger = datasets.logging.get_logger(__name__) |
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_DESCRIPTION = """\ |
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CrowdSpeech is a publicly available large-scale dataset of crowdsourced audio transcriptions. \ |
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It contains annotations for more than 50 hours of English speech transcriptions from more \ |
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than 1,000 crowd workers. |
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""" |
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_URL = "https://raw.githubusercontent.com/pilot7747/VoxDIY/main/data/huggingface/" |
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_URLS = { |
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"train-clean": _URL + "train-clean.json", |
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"dev-clean": _URL + "dev-clean.json", |
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"dev-other": _URL + "dev-other.json", |
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"test-clean": _URL + "test-clean.json", |
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"test-other": _URL + "test-other.json", |
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} |
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class CrowdSpeechConfig(datasets.BuilderConfig): |
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"""BuilderConfig for CrowdSpeech.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for CrowdSpeech. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(CrowdSpeechConfig, self).__init__(**kwargs) |
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class CrowdSpeech(datasets.GeneratorBasedBuilder): |
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"""CrowdSpeech: Benchmark Dataset for Crowdsourced Audio Transcription.""" |
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BUILDER_CONFIGS = [ |
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CrowdSpeechConfig( |
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name="plain_text", |
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version=datasets.Version("1.0.0", ""), |
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description="Plain text", |
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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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"task": datasets.Value("string"), |
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"transcriptions": datasets.Value("string"), |
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"performers": datasets.Value("string"), |
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"gt": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://github.com/pilot7747/VoxDIY/", |
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task_templates=[ |
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Summarization( |
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text_column="transcriptions", summary_column="gt" |
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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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downloaded_files = dl_manager.download_and_extract(_URLS) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train-clean"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test-clean"]}), |
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datasets.SplitGenerator(name='test-other', gen_kwargs={"filepath": downloaded_files["test-other"]}), |
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datasets.SplitGenerator(name='dev-clean', gen_kwargs={"filepath": downloaded_files["dev-clean"]}), |
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datasets.SplitGenerator(name='dev-other', gen_kwargs={"filepath": downloaded_files["dev-clean"]}), |
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] |
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def _generate_examples(self, filepath): |
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"""This function returns the examples in the raw (text) form.""" |
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logger.info("generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as f: |
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crowdspeech = json.load(f) |
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for audio in crowdspeech["data"]: |
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task = audio.get("task", "") |
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transcriptions = audio.get("transcriptions", "") |
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performers = audio.get("performers", "") |
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gt = audio.get("gt", "") |
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yield task, { |
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"transcriptions": transcriptions, |
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"performers": performers, |
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"gt": gt, |
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} |
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