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