| 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") |
|
|
| |
| waveform, sample_rate = torchaudio.load(fileid_audio) |
|
|
| return ( |
| waveform, |
| sample_rate, |
| transcript, |
| normalized_transcript, |
| ) |
|
|
| def __len__(self) -> int: |
| return len(self._flist) |
|
|
