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