pwr-azon-speech-dataset_depr.py

# coding=utf-8
"""PWr AZON Speech Dataset"""

import pathlib

import datasets


_DESCRIPTION = """\
TODO
"""
_HOMEPAGE = ""
_CITATION = ""
_LICENSE = "CC BY-SA 4.0"



class PWrAZON(datasets.GeneratorBasedBuilder):

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "audio": datasets.Audio(sampling_rate=44_100),
                    "transcript": datasets.Value("string"),
                    "gender": datasets.Value("string"),
                    "id": datasets.Value("string"),
                    "id_og": datasets.Value("string"),
                },
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):
        metadata_train_path = dl_manager.download_and_extract(
            "https://huggingface.co/datasets/czyzi0/pwr-azon-speech-dataset/raw/main/metadata_train.tsv"
        )
        metadata_unsup_path = dl_manager.download_and_extract(
            "https://huggingface.co/datasets/czyzi0/pwr-azon-speech-dataset/raw/main/metadata_unsup.tsv"
        )
        wavs_train_path = dl_manager.download_and_extract(
            "https://huggingface.co/datasets/czyzi0/pwr-azon-speech-dataset/resolve/main/wavs_train.tar.gz"
        )
        wavs_unsup_path = dl_manager.download_and_extract(
            "https://huggingface.co/datasets/czyzi0/pwr-azon-speech-dataset/resolve/main/wavs_unsup.tar.gz"
        )
        return [
            datasets.SplitGenerator(
                name="train",
                gen_kwargs={
                    "metadata_path": metadata_train_path,
                    "wavs_path": pathlib.Path(wavs_train_path) / "wavs_train",
                },
            ),
            datasets.SplitGenerator(
                name="unsup",
                gen_kwargs={
                    "metadata_path": metadata_unsup_path,
                    "wavs_path": pathlib.Path(wavs_unsup_path) / "wavs_unsup",
                },
            ),
        ]

    def _generate_examples(self, metadata_path, wavs_path):
        with open(metadata_path, "r") as fh:
            header = next(fh).strip().split("\t")
            for item_idx, line in enumerate(fh):
                line = line.strip().split("\t")
                id_ = line[header.index("id")]
                transcript = line[header.index("transcript")]
                gender = line[header.index("gender")]
                id_og = line[header.index("id_og")]

                wav_path = wavs_path / f"{id_}.wav"
                with open(wav_path, "rb") as fh_:
                    item = {
                        "audio": {"path": str(wav_path.absolute()), "bytes": fh_.read()},
                        "transcript": transcript,
                        "gender": gender,
                        "id": id_,
                        "id_og": id_og,
                    }
                    yield item_idx, item