Source code for torchaudio.datasets.cmuarctic

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

import torchaudio
from torch import Tensor
from torch.hub import download_url_to_file
from import Dataset
from torchaudio.datasets.utils import _extract_tar

URL = "aew"
    "": "645cb33c0f0b2ce41384fdd8d3db2c3f5fc15c1e688baeb74d2e08cab18ab406",  # noqa: E501
    "": "024664adeb892809d646a3efd043625b46b5bfa3e6189b3500b2d0d59dfab06c",  # noqa: E501
    "": "2c55bc3050caa996758869126ad10cf42e1441212111db034b3a45189c18b6fc",  # noqa: E501
    "": "d74a950c9739a65f7bfc4dfa6187f2730fa03de5b8eb3f2da97a51b74df64d3c",  # noqa: E501
    "": "dd65c3d2907d1ee52f86e44f578319159e60f4bf722a9142be01161d84e330ff",  # noqa: E501
    "": "26b91aaf48b2799b2956792b4632c2f926cd0542f402b5452d5adecb60942904",  # noqa: E501
    "": "3f16dc3f3b97955ea22623efb33b444341013fc660677b2e170efdcc959fa7c6",  # noqa: E501
    "": "8a0ee4e5acbd4b2f61a4fb947c1730ab3adcc9dc50b195981d99391d29928e8a",  # noqa: E501
    "": "3fcff629412b57233589cdb058f730594a62c4f3a75c20de14afe06621ef45e2",  # noqa: E501
    "": "dc82e7967cbd5eddbed33074b0699128dbd4482b41711916d58103707e38c67f",  # noqa: E501
    "": "3a37c0e1dfc91e734fdbc88b562d9e2ebca621772402cdc693bbc9b09b211d73",  # noqa: E501
    "": "8029cafce8296f9bed3022c44ef1e7953332b6bf6943c14b929f468122532717",  # noqa: E501
    "": "b23993765cbf2b9e7bbc3c85b6c56eaf292ac81ee4bb887b638a24d104f921a0",  # noqa: E501
    "": "4faf34d71aa7112813252fb20c5433e2fdd9a9de55a00701ffcbf05f24a5991a",  # noqa: E501
    "": "c6dc11235629c58441c071a7ba8a2d067903dfefbaabc4056d87da35b72ecda4",  # noqa: E501
    "": "1fa4271c393e5998d200e56c102ff46fcfea169aaa2148ad9e9469616fbfdd9b",  # noqa: E501
    "": "54345ed55e45c23d419e9a823eef427f1cc93c83a710735ec667d068c916abf1",  # noqa: E501
    "": "7c173297916acf3cc7fcab2713be4c60b27312316765a90934651d367226b4ea",  # noqa: E501

def load_cmuarctic_item(line: str, path: str, folder_audio: str, ext_audio: str) -> Tuple[Tensor, int, str, str]:

    utterance_id, transcript = line[0].strip().split(" ", 2)[1:]

    # Remove space, double quote, and single parenthesis from transcript
    transcript = transcript[1:-3]

    file_audio = os.path.join(path, folder_audio, utterance_id + ext_audio)

    # Load audio
    waveform, sample_rate = torchaudio.load(file_audio)

    return (waveform, sample_rate, transcript, utterance_id.split("_")[1])

[docs]class CMUARCTIC(Dataset): """*CMU ARCTIC* :cite:`Kominek03cmuarctic` 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 or the type of the dataset to download. (default: ``"aew"``) Allowed type values are ``"aew"``, ``"ahw"``, ``"aup"``, ``"awb"``, ``"axb"``, ``"bdl"``, ``"clb"``, ``"eey"``, ``"fem"``, ``"gka"``, ``"jmk"``, ``"ksp"``, ``"ljm"``, ``"lnh"``, ``"rms"``, ``"rxr"``, ``"slp"`` or ``"slt"``. folder_in_archive (str, optional): The top-level directory of the dataset. (default: ``"ARCTIC"``) download (bool, optional): Whether to download the dataset if it is not found at root path. (default: ``False``). """ _file_text = "" _folder_text = "etc" _ext_audio = ".wav" _folder_audio = "wav" def __init__( self, root: Union[str, Path], url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False ) -> None: if url in [ "aew", "ahw", "aup", "awb", "axb", "bdl", "clb", "eey", "fem", "gka", "jmk", "ksp", "ljm", "lnh", "rms", "rxr", "slp", "slt", ]: url = "cmu_us_" + url + "_arctic" ext_archive = ".tar.bz2" base_url = "" url = os.path.join(base_url, url + ext_archive) # Get string representation of 'root' in case Path object is passed root = os.fspath(root) basename = os.path.basename(url) root = os.path.join(root, folder_in_archive) if not os.path.isdir(root): os.mkdir(root) archive = os.path.join(root, basename) basename = basename.split(".")[0] self._path = os.path.join(root, basename) if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): checksum = _CHECKSUMS.get(url, None) 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" ) self._text = os.path.join(self._path, self._folder_text, self._file_text) with open(self._text, "r") as text: walker = csv.reader(text, delimiter="\n") self._walker = list(walker)
[docs] 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: Utterance ID """ line = self._walker[n] return load_cmuarctic_item(line, self._path, self._folder_audio, self._ext_audio)
def __len__(self) -> int: return len(self._walker)


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