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Source code for torchaudio.datasets.speechcommands

import os
from typing import Tuple

import torchaudio
from torch.utils.data import Dataset
from torch import Tensor
from torchaudio.datasets.utils import (
    download_url,
    extract_archive,
    walk_files
)

FOLDER_IN_ARCHIVE = "SpeechCommands"
URL = "speech_commands_v0.02"
HASH_DIVIDER = "_nohash_"
EXCEPT_FOLDER = "_background_noise_"
_CHECKSUMS = {
    "https://storage.googleapis.com/download.tensorflow.org/data/speech_commands_v0.01.tar.gz":
    "3cd23799cb2bbdec517f1cc028f8d43c",
    "https://storage.googleapis.com/download.tensorflow.org/data/speech_commands_v0.02.tar.gz":
    "6b74f3901214cb2c2934e98196829835",
}


def load_speechcommands_item(filepath: str, path: str) -> Tuple[Tensor, int, str, str, int]:
    relpath = os.path.relpath(filepath, path)
    label, filename = os.path.split(relpath)
    speaker, _ = os.path.splitext(filename)

    speaker_id, utterance_number = speaker.split(HASH_DIVIDER)
    utterance_number = int(utterance_number)

    # Load audio
    waveform, sample_rate = torchaudio.load(filepath)
    return waveform, sample_rate, label, speaker_id, utterance_number


[docs]class SPEECHCOMMANDS(Dataset): """Create a Dataset for Speech Commands. Args: root (str): 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 dowload. Allowed type values are ``"speech_commands_v0.01"`` and ``"speech_commands_v0.02"`` (default: ``"speech_commands_v0.02"``) folder_in_archive (str, optional): The top-level directory of the dataset. (default: ``"SpeechCommands"``) download (bool, optional): Whether to download the dataset if it is not found at root path. (default: ``False``). """ def __init__(self, root: str, url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False) -> None: if url in [ "speech_commands_v0.01", "speech_commands_v0.02", ]: base_url = "https://storage.googleapis.com/download.tensorflow.org/data/" ext_archive = ".tar.gz" url = os.path.join(base_url, url + ext_archive) basename = os.path.basename(url) archive = os.path.join(root, basename) basename = basename.rsplit(".", 2)[0] folder_in_archive = os.path.join(folder_in_archive, basename) self._path = os.path.join(root, folder_in_archive) if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): checksum = _CHECKSUMS.get(url, None) download_url(url, root, hash_value=checksum, hash_type="md5") extract_archive(archive, self._path) walker = walk_files(self._path, suffix=".wav", prefix=True) walker = filter(lambda w: HASH_DIVIDER in w and EXCEPT_FOLDER not in w, walker) self._walker = list(walker)
[docs] def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, int]: """Load the n-th sample from the dataset. Args: n (int): The index of the sample to be loaded Returns: tuple: ``(waveform, sample_rate, label, speaker_id, utterance_number)`` """ fileid = self._walker[n] return load_speechcommands_item(fileid, self._path)
def __len__(self) -> int: return len(self._walker)

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