izzy-lazerson
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Browse files- README.md +25 -0
- dataset_infos.json +23 -0
- rakeffet.py +137 -0
README.md
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---
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pretty_name: Rakeffet
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annotations_creators:
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- expert-generated
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language_creators:
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- expert-generated
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language:
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- en
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license:
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- cc-by-nc-4.0
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multilinguality:
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- monolingual
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source_datasets:
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- original
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task_categories:
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- automatic-speech-recognition
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- audio-classification
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- speech-synthesis
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---
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# Dataset Card for Rakeffet
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dataset_infos.json
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{
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"description": "rakeffet",
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"homepage": "google.com",
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"supervised_keys": {
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"input": "id",
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"output": "text"
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},
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"task_templates": [
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{
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"task": "automatic-speech-recognition",
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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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"builder_name": "rakeffet",
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"version": {
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"version_str": "1.0.0",
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"description": "",
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"major": 2,
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"minor": 1,
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"patch": 0
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}
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}
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rakeffet.py
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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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# (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
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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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