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| import csv | |
| import os | |
| from pathlib import Path | |
| from typing import Tuple, Union | |
| import torchaudio | |
| from torch import Tensor | |
| from torch.utils.data import Dataset | |
| from torchaudio._internal import download_url_to_file | |
| from torchaudio.datasets.utils import _extract_tar | |
| _RELEASE_CONFIGS = { | |
| "release1": { | |
| "folder_in_archive": "wavs", | |
| "url": "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2", | |
| "checksum": "be1a30453f28eb8dd26af4101ae40cbf2c50413b1bb21936cbcdc6fae3de8aa5", | |
| } | |
| } | |
| class LJSPEECH(Dataset): | |
| """*LJSpeech-1.1* :cite:`ljspeech17` dataset. | |
| Args: | |
| root (str or Path): Path to the directory where the dataset is found or downloaded. | |
| url (str, optional): The URL to download the dataset from. | |
| (default: ``"https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2"``) | |
| folder_in_archive (str, optional): | |
| The top-level directory of the dataset. (default: ``"wavs"``) | |
| download (bool, optional): | |
| Whether to download the dataset if it is not found at root path. (default: ``False``). | |
| """ | |
| def __init__( | |
| self, | |
| root: Union[str, Path], | |
| url: str = _RELEASE_CONFIGS["release1"]["url"], | |
| folder_in_archive: str = _RELEASE_CONFIGS["release1"]["folder_in_archive"], | |
| download: bool = False, | |
| ) -> None: | |
| self._parse_filesystem(root, url, folder_in_archive, download) | |
| def _parse_filesystem(self, root: str, url: str, folder_in_archive: str, download: bool) -> None: | |
| root = Path(root) | |
| basename = os.path.basename(url) | |
| archive = root / basename | |
| basename = Path(basename.split(".tar.bz2")[0]) | |
| folder_in_archive = basename / folder_in_archive | |
| self._path = root / folder_in_archive | |
| self._metadata_path = root / basename / "metadata.csv" | |
| if download: | |
| if not os.path.isdir(self._path): | |
| if not os.path.isfile(archive): | |
| checksum = _RELEASE_CONFIGS["release1"]["checksum"] | |
| download_url_to_file(url, archive, hash_prefix=checksum) | |
| _extract_tar(archive) | |
| else: | |
| if not os.path.exists(self._path): | |
| raise RuntimeError( | |
| f"The path {self._path} doesn't exist. " | |
| "Please check the ``root`` path or set `download=True` to download it" | |
| ) | |
| with open(self._metadata_path, "r", newline="") as metadata: | |
| flist = csv.reader(metadata, delimiter="|", quoting=csv.QUOTE_NONE) | |
| self._flist = list(flist) | |
| def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]: | |
| """Load the n-th sample from the dataset. | |
| Args: | |
| n (int): The index of the sample to be loaded | |
| Returns: | |
| Tuple of the following items; | |
| Tensor: | |
| Waveform | |
| int: | |
| Sample rate | |
| str: | |
| Transcript | |
| str: | |
| Normalized Transcript | |
| """ | |
| line = self._flist[n] | |
| fileid, transcript, normalized_transcript = line | |
| fileid_audio = self._path / (fileid + ".wav") | |
| # Load audio | |
| waveform, sample_rate = torchaudio.load(fileid_audio) | |
| return ( | |
| waveform, | |
| sample_rate, | |
| transcript, | |
| normalized_transcript, | |
| ) | |
| def __len__(self) -> int: | |
| return len(self._flist) | |