Source code for torchaudio.datasets.commonvoice

import csv
import os
from pathlib import Path
from typing import Dict, List, Tuple, Union

import torchaudio
from torch import Tensor
from torch.utils.data import Dataset


def load_commonvoice_item(
    line: List[str], header: List[str], path: str, folder_audio: str, ext_audio: str
) -> Tuple[Tensor, int, Dict[str, str]]:
    # Each line as the following data:
    # client_id, path, sentence, up_votes, down_votes, age, gender, accent

    assert header[1] == "path"
    fileid = line[1]
    filename = os.path.join(path, folder_audio, fileid)
    if not filename.endswith(ext_audio):
        filename += ext_audio
    waveform, sample_rate = torchaudio.load(filename)

    dic = dict(zip(header, line))

    return waveform, sample_rate, dic


class COMMONVOICE(Dataset):
    """Create a Dataset for *CommonVoice* [:footcite:`ardila2020common`].

    Args:
        root (str or Path): Path to the directory where the dataset is located.
             (Where the ``tsv`` file is present.)
        tsv (str, optional):
            The name of the tsv file used to construct the metadata, such as
            ``"train.tsv"``, ``"test.tsv"``, ``"dev.tsv"``, ``"invalidated.tsv"``,
            ``"validated.tsv"`` and ``"other.tsv"``. (default: ``"train.tsv"``)
    """

    _ext_txt = ".txt"
    _ext_audio = ".mp3"
    _folder_audio = "clips"

    def __init__(self, root: Union[str, Path], tsv: str = "train.tsv") -> None:

        # Get string representation of 'root' in case Path object is passed
        self._path = os.fspath(root)
        self._tsv = os.path.join(self._path, tsv)

        with open(self._tsv, "r") as tsv_:
            walker = csv.reader(tsv_, delimiter="\t")
            self._header = next(walker)
            self._walker = list(walker)

    def __getitem__(self, n: int) -> Tuple[Tensor, int, Dict[str, str]]:
        """Load the n-th sample from the dataset.

        Args:
            n (int): The index of the sample to be loaded

        Returns:
            (Tensor, int, Dict[str, str]): ``(waveform, sample_rate, dictionary)``,  where dictionary
            is built from the TSV file with the following keys: ``client_id``, ``path``, ``sentence``,
            ``up_votes``, ``down_votes``, ``age``, ``gender`` and ``accent``.
        """
        line = self._walker[n]
        return load_commonvoice_item(line, self._header, self._path, self._folder_audio, self._ext_audio)

    def __len__(self) -> int:
        return len(self._walker)