def SpeechCommands(
    root=default_dataset_location("speech_commands"),
    url="speech_commands_v0.02",
    download=True,
    subset=None,
    mfcc_preprocessing=None,
):
    """
    root: dataset root location
    url: version name of the dataset
    download: automatically download the dataset, if not present
    subset: one of 'training', 'validation', 'testing'
    mfcc_preprocessing: an optional torchaudio.transforms.MFCC instance
        to preprocess each audio. Warning: this may slow down the execution
        since preprocessing is applied on-the-fly each time a sample is
        retrieved from the dataset.
    """
    dataset = SpeechCommandsData(
        root=root,
        download=download,
        subset=subset,
        url=url,
        mfcc_preprocessing=mfcc_preprocessing,
    )
    labels = [datapoint[1] for datapoint in dataset]
    return _make_taskaware_classification_dataset(
        dataset, collate_fn=speech_commands_collate, targets=labels
    )

    root: dataset root location
    url: version name of the dataset
    download: automatically download the dataset, if not present
    subset: one of 'training', 'validation', 'testing'
    mfcc_preprocessing: an optional torchaudio.transforms.MFCC instance
        to preprocess each audio. Warning: this may slow down the execution
        since preprocessing is applied on-the-fly each time a sample is
        retrieved from the dataset.
    
avalanche/benchmarks/datasets/torchaudio_wrapper.py