Source code for torchaudio.datasets.quesst14

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

import torch
import torchaudio
from torch.hub import download_url_to_file
from import Dataset
from torchaudio.datasets.utils import extract_archive

URL = ""
_CHECKSUM = "4f869e06bc066bbe9c5dde31dbd3909a0870d70291110ebbb38878dcbc2fc5e4"

[docs]class QUESST14(Dataset): """Create *QUESST14* [:footcite:`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: assert subset in ["docs", "dev", "eval"], "`subset` must be one of ['docs', 'dev', 'eval']" assert language is None or language in _LANGUAGES, 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": = filter_audio_paths(self._path, language, "language_key_utterances.lst") elif subset == "dev": = filter_audio_paths(self._path, language, "language_key_dev.lst") elif subset == "eval": = filter_audio_paths(self._path, language, "language_key_eval.lst") def _load_sample(self, n: int) -> Tuple[torch.Tensor, int, str]: audio_path =[n] wav, sample_rate = torchaudio.load(audio_path) return wav, 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: (Tensor, int, str): ``(waveform, sample_rate, file_name)`` """ return self._load_sample(n)
def __len__(self) -> int: return len(
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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