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Source code for torchaudio.datasets.quesst14

import os
import re
from pathlib import Path
from typing import Optional, Tuple, Union

import torch
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import _load_waveform, extract_archive


URL = "https://speech.fit.vutbr.cz/files/quesst14Database.tgz"
SAMPLE_RATE = 8000
_CHECKSUM = "4f869e06bc066bbe9c5dde31dbd3909a0870d70291110ebbb38878dcbc2fc5e4"
_LANGUAGES = [
    "albanian",
    "basque",
    "czech",
    "nnenglish",
    "romanian",
    "slovak",
]


[docs]class QUESST14(Dataset): """*QUESST14* :cite:`Mir2015QUESST2014EQ` dataset. Args: root (str or Path): Root directory where the dataset's top level directory is found subset (str): Subset of the dataset to use. Options: [``"docs"``, ``"dev"``, ``"eval"``]. language (str or None, optional): Language to get dataset for. Options: [``None``, ``albanian``, ``basque``, ``czech``, ``nnenglish``, ``romanian``, ``slovak``]. If ``None``, dataset consists of all languages. (default: ``"nnenglish"``) 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], subset: str, language: Optional[str] = "nnenglish", download: bool = False, ) -> None: if subset not in ["docs", "dev", "eval"]: raise ValueError("`subset` must be one of ['docs', 'dev', 'eval']") if language is not None and language not in _LANGUAGES: raise ValueError(f"`language` must be None or one of {str(_LANGUAGES)}") # Get string representation of 'root' root = os.fspath(root) basename = os.path.basename(URL) archive = os.path.join(root, basename) basename = basename.rsplit(".", 2)[0] self._path = os.path.join(root, basename) if not os.path.isdir(self._path): if not os.path.isfile(archive): if not download: raise RuntimeError("Dataset not found. Please use `download=True` to download") download_url_to_file(URL, archive, hash_prefix=_CHECKSUM) extract_archive(archive, root) if subset == "docs": self.data = filter_audio_paths(self._path, language, "language_key_utterances.lst") elif subset == "dev": self.data = filter_audio_paths(self._path, language, "language_key_dev.lst") elif subset == "eval": self.data = filter_audio_paths(self._path, language, "language_key_eval.lst")
[docs] def get_metadata(self, n: int) -> Tuple[str, int, str]: """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform, but otherwise returns the same fields as :py:func:`__getitem__`. Args: n (int): The index of the sample to be loaded Returns: Tuple of the following items; str: Path to audio int: Sample rate str: File name """ audio_path = self.data[n] relpath = os.path.relpath(audio_path, self._path) return relpath, SAMPLE_RATE, audio_path.with_suffix("").name
[docs] def __getitem__(self, n: int) -> Tuple[torch.Tensor, int, 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: File name """ metadata = self.get_metadata(n) waveform = _load_waveform(self._path, metadata[0], metadata[1]) return (waveform,) + metadata[1:]
def __len__(self) -> int: return len(self.data)
def filter_audio_paths( path: str, language: str, lst_name: str, ): """Extract audio paths for the given language.""" audio_paths = [] path = Path(path) with open(path / "scoring" / lst_name) as f: for line in f: audio_path, lang = line.strip().split() if language is not None and lang != language: continue audio_path = re.sub(r"^.*?\/", "", audio_path) audio_paths.append(path / audio_path) return audio_paths

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