90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
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import os
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from pathlib import Path
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from typing import List, Tuple, Union
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import torchaudio
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from torch import Tensor
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from torch.utils.data import Dataset
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from torchaudio._internal import download_url_to_file
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from torchaudio.datasets.utils import _extract_tar
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_RELEASE_CONFIGS = {
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"release1": {
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"folder_in_archive": "waves_yesno",
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"url": "http://www.openslr.org/resources/1/waves_yesno.tar.gz",
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"checksum": "c3f49e0cca421f96b75b41640749167b52118f232498667ca7a5f9416aef8e73",
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}
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}
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class YESNO(Dataset):
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"""*YesNo* :cite:`YesNo` dataset.
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Args:
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root (str or Path): Path to the directory where the dataset is found or downloaded.
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url (str, optional): The URL to download the dataset from.
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(default: ``"http://www.openslr.org/resources/1/waves_yesno.tar.gz"``)
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folder_in_archive (str, optional):
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The top-level directory of the dataset. (default: ``"waves_yesno"``)
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download (bool, optional):
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Whether to download the dataset if it is not found at root path. (default: ``False``).
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"""
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def __init__(
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self,
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root: Union[str, Path],
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url: str = _RELEASE_CONFIGS["release1"]["url"],
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folder_in_archive: str = _RELEASE_CONFIGS["release1"]["folder_in_archive"],
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download: bool = False,
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) -> None:
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self._parse_filesystem(root, url, folder_in_archive, download)
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def _parse_filesystem(self, root: str, url: str, folder_in_archive: str, download: bool) -> None:
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root = Path(root)
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archive = os.path.basename(url)
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archive = root / archive
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self._path = root / folder_in_archive
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if download:
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if not os.path.isdir(self._path):
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if not os.path.isfile(archive):
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checksum = _RELEASE_CONFIGS["release1"]["checksum"]
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download_url_to_file(url, archive, hash_prefix=checksum)
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_extract_tar(archive)
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if not os.path.isdir(self._path):
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raise RuntimeError("Dataset not found. Please use `download=True` to download it.")
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self._walker = sorted(str(p.stem) for p in Path(self._path).glob("*.wav"))
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def _load_item(self, fileid: str, path: str):
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labels = [int(c) for c in fileid.split("_")]
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file_audio = os.path.join(path, fileid + ".wav")
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waveform, sample_rate = torchaudio.load(file_audio)
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return waveform, sample_rate, labels
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def __getitem__(self, n: int) -> Tuple[Tensor, int, List[int]]:
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"""Load the n-th sample from the dataset.
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Args:
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n (int): The index of the sample to be loaded
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Returns:
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Tuple of the following items;
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Tensor:
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Waveform
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int:
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Sample rate
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List[int]:
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labels
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"""
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fileid = self._walker[n]
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item = self._load_item(fileid, self._path)
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return item
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def __len__(self) -> int:
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return len(self._walker)
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