the-mc-speech-dataset_depr.py

# coding=utf-8
"""The MC Speech Dataset"""

import pathlib

import datasets


_DESCRIPTION = """\
This is public domain speech dataset consisting of 24018 short audio clips of a single speaker
reading sentences in Polish. A transcription is provided for each clip. Clips have total length of
more than 22 hours.
Texts are in public domain. The audio was recorded in 2021-22 as a part of my master's thesis and
is in public domain.
"""
_HOMEPAGE = "https://github.com/czyzi0/the-mc-speech-dataset"
_CITATION = """\
@masterthesis{mcspeech,
  title={Analiza porównawcza korpusów nagrań mowy dla celów syntezy mowy w języku polskim},
  author={Czyżnikiewicz, Mateusz},
  year={2022},
  month={December},
  school={Warsaw University of Technology},
  type={Master's thesis},
  doi={10.13140/RG.2.2.26293.24800},
  note={Available at \\url{http://dx.doi.org/10.13140/RG.2.2.26293.24800}},
}
"""
_LICENSE = "CC0 1.0"



class MCSpeech(datasets.GeneratorBasedBuilder):

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

    def _split_generators(self, dl_manager):
        transcripts_path = dl_manager.download_and_extract(
            "https://huggingface.co/datasets/czyzi0/the-mc-speech-dataset/raw/main/transcripts.tsv"
        )
        wavs_path = dl_manager.download_and_extract(
            "https://huggingface.co/datasets/czyzi0/the-mc-speech-dataset/resolve/main/wavs.tar.gz"
        )
        return [
            datasets.SplitGenerator(
                name="train",
                gen_kwargs={"transcripts_path": transcripts_path, "wavs_path": wavs_path}
            )
        ]

    def _generate_examples(self, transcripts_path, wavs_path):
        wavs_path = pathlib.Path(wavs_path)
        with open(transcripts_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")]

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