system: You are an expert security code reviewer. You will be shown a source code file. Your task is to name the single function or class that contains the specified CWE vulnerability.

OUTPUT FORMAT: Respond with the exact function or class name (no parentheses, no qualifiers, no module prefixes), wrapped in \boxed{...}. Example: `\boxed{salted_hmac}`. Output the boxed name ONLY -- no explanation, no other text.

user: ```python
"""
About the speech_commands corpus
This is a set of one-second .wav audio files, each containing a single spoken English word or background noise.
These words are from a small set of commands, and are spoken by a variety of different speakers.
This data set is designed to help train simple machine learning models.
It is covered in more detail at https://arxiv.org/abs/1804.03209.

Version 0.01 of the data set (configuration "v0.01") was released on August 3rd 2017 and contains 64,727 audio files.
Version 0.02 of the data set (configuration "v0.02") was released on April 11th 2018 and contains 105,829 audio files.
"""

import logging
import os
import shutil
import tarfile
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Optional, Sequence, Tuple, Union

from tqdm.auto import tqdm

from lhotse import fix_manifests, validate_recordings_and_supervisions
from lhotse.audio import Recording, RecordingSet
from lhotse.recipes.utils import manifests_exist, read_manifests_if_cached
from lhotse.supervision import SupervisionSegment, SupervisionSet
from lhotse.utils import Pathlike, is_module_available, resumable_download, safe_extract

_DOWNLOAD_PATH_V1 = "http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz"
_TEST_DOWNLOAD_PATH_V1 = (
    "http://download.tensorflow.org/data/speech_commands_test_set_v0.01.tar.gz"
)
_DOWNLOAD_PATH_V2 = "http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz"
_TEST_DOWNLOAD_PATH_V2 = (
    "http://download.tensorflow.org/data/speech_commands_test_set_v0.02.tar.gz"
)

_SPLITS = ["train", "valid", "test"]

WORDS = ["down", "go", "left", "no", "off", "on", "right", "stop", "up", "yes"]
SILENCE = "_silence_"
UNKNOWN = "_unknown_"
BACKGROUND_NOISE = "_background_noise_"


def download_speechcommands(
    speechcommands_version: str = "2",
    target_dir: Pathlike = ".",
    force_download: bool = False,
) -> Path:
    """
    Download and unzip Speech Commands dataset

    :param speechcommands_version: str, dataset version.
    :param target_dir: Pathlike, the path of the dir to store the dataset.
    :param force_download: bool, if True, download the archive even if it already exists.

    :return: the path to downloaded and extracted directory with data.
    """
    target_dir = Path(target_dir)
    corpus_dir = target_dir / f"SpeechCommands{speechcommands_version}"
    corpus_dir.mkdir(parents=True, exist_ok=True)

    dataset_parts = (
        f"speech_commands_v0.0{speechcommands_version}",
        f"speech_commands_test_set_v0.0{speechcommands_version}",
    )

    for part in tqdm(
        dataset_parts, desc=f"Downloading Speech Commands v0.0{speechcommands_version}"
    ):
        logging.info(f"Processing split: {part}")
        part_dir = corpus_dir / part
        completed_detector = part_dir / ".completed"
        if completed_detector.is_file():
            logging.info(f"Skipping {part} because {completed_detector} exists.")
            continue
        # Process the archive.
        tar_name = f"{part}.tar.gz"
        tar_path = corpus_dir / tar_name
        resumable_download(
            f"http://download.tensorflow.org/data/{tar_name}",
            filename=tar_path,
            force_download=force_download,
        )
        # Remove partial unpacked files, if any, and unpack everything.
        shutil.rmtree(part_dir, ignore_errors=True)
        with tarfile.open(tar_path) as tar:
            tar.extractall(part_dir)
        completed_detector.touch()

    return corpus_dir


def _split_train_valid(
    speechcommands_version: str,
    corpus_dir: Pathlike,
) -> Tuple[set, set]:
    """
    Returns the train_paths and valid_paths given a dataset part.
    :param speechcommands_version: str, dataset version.
    :param corpus_dir: Pathlike, the path of the data dir.
    :return: the train_paths and valid_paths.
    """
    corpus_dir = Path(corpus_dir)
    part_path = corpus_dir / f"speech_commands_v0.0{speechcommands_version}"

    # Split dataset into train and valid
    train_paths = []
    for root in os.listdir(part_path):
        if root == "testing_list.txt":
            with open(part_path / root) as file_obj:
                train_test_paths = file_obj.read().strip().splitlines()
        elif root == "validation_list.txt":
            with open(part_path / root) as file_obj:
                valid_paths = file_obj.read().strip().splitlines()
        elif os.path.isdir(part_path / root):
            file_paths = os.listdir(part_path / root)
            train_paths += [
                os.path.join(root, file_path)
                for file_path in file_paths
                if file_path.endswith(".wav")
            ]

    # Original validation files don't include silence - we add them manually here
    valid_paths.append(os.path.join(BACKGROUND_NOISE, "running_tap.wav"))

    # The paths for the train set is just whichever paths that do not exist in either the test or validation splits.
    train_paths = set(train_paths) - set(valid_paths) - set(train_test_paths)

    return train_paths, valid_paths


def _prepare_train(
    speechcommands_version: str,
    train_paths: set,
    corpus_dir: Pathlike,
) -> Tuple[RecordingSet, SupervisionSet]:
    """
    Returns the RecodingSet and SupervisionSet given a dataset part.
    :param speechcommands_version: str, dataset version.
    :param train_paths: set, the train subset paths.
    :param corpus_dir: Pathlike, the path of the data dir.
    :return: the RecodingSet and SupervisionSet for train and valid.
    """
    corpus_dir = Path(corpus_dir)
    part_path = corpus_dir / f"speech_commands_v0.0{speechcommands_version}"

    train_recordings = []
    train_supervisions = []
    for train_path in train_paths:
        audio_path = part_path / train_path
        audio_path = audio_path.resolve()
        train_path_splits = train_path.split("/")
        audio_file_name = train_path.replace("/", "_").replace(".wav", "")
        audio_file_name_splits = train_path_splits[1].split("_")

        if train_path_splits[0] == BACKGROUND_NOISE:
            speaker = None
            text = ""
        else:
            speaker = audio_file_name_splits[0]
            text = train_path_splits[0].strip()
        if not audio_path.is_file():
            logging.warning(f"No such file: {audio_path}")
            continue
        train_recording = Recording.from_file(
            path=audio_path,
            recording_id=audio_file_name,
        )
        train_recordings.append(train_recording)
        train_segment = SupervisionSegment(
            id=audio_file_name,
            recording_id=audio_file_name,
            start=0.0,
            duration=train_recording.duration,
            channel=0,
            language="English",
            speaker=speaker,
            text=text,
        )
        train_supervisions.append(train_segment)
    train_recording_set = RecordingSet.from_recordings(train_recordings)
    train_supervision_set = SupervisionSet.from_segments(train_supervisions)

    # Fix manifests
    train_recording_set, train_supervision_set = fix_manifests(
        train_recording_set, train_supervision_set
    )
    validate_recordings_and_supervisions(train_recording_set, train_supervision_set)

    return train_recording_set, train_supervision_set


def _prepare_valid(
    speechcommands_version: str,
    valid_paths: set,
    corpus_dir: Pathlike,
) -> Tuple[RecordingSet, SupervisionSet]:
    """
    Returns the RecodingSet and SupervisionSet given a dataset part.
    :param speechcommands_version: str, dataset version.
    :param valid_paths: set, the valid subset paths.
    :param corpus_dir: Pathlike, the path of the data dir.
    :return: the RecodingSet and SupervisionSet for train and valid.
    """
    corpus_dir = Path(corpus_dir)
    part_path = corpus_dir / f"speech_commands_v0.0{speechcommands_version}"

    valid_recordings = []
    valid_supervisions = []
    for valid_path in valid_paths:
        audio_path = part_path / valid_path
        audio_path = audio_path.resolve()
        valid_path_splits = valid_path.split("/")
        audio_file_name = valid_path.replace("/", "_").replace(".wav", "")
        audio_file_name_splits = valid_path_splits[1].split("_")

        if valid_path_splits[0] == BACKGROUND_NOISE:
            speaker = None
            text = ""
        else:
            speaker = audio_file_name_splits[0]
            text = valid_path_splits[0].strip()

        if not audio_path.is_file():
            logging.warning(f"No such file: {audio_path}")
            continue
        valid_recording = Recording.from_file(
            path=audio_path,
            recording_id=audio_file_name,
        )
        valid_recordings.append(valid_recording)
        valid_segment = SupervisionSegment(
            id=audio_file_name,
            recording_id=audio_file_name,
            start=0.0,
            duration=valid_recording.duration,
            channel=0,
            language="English",
            speaker=speaker,
            text=text,
        )
        valid_supervisions.append(valid_segment)
    valid_recording_set = RecordingSet.from_recordings(valid_recordings)
    valid_supervision_set = SupervisionSet.from_segments(valid_supervisions)

    # Fix manifests
    valid_recording_set, valid_supervision_set = fix_manifests(
        valid_recording_set, valid_supervision_set
    )
    validate_recordings_and_supervisions(valid_recording_set, valid_supervision_set)

    return valid_recording_set, valid_supervision_set


def _prepare_test(
    speechcommands_version: str,
    corpus_dir: Pathlike,
) -> Tuple[RecordingSet, SupervisionSet]:
    """
    Returns the RecodingSet and SupervisionSet given a dataset part.
    :param speechcommands_version: str, dataset version.
    :param corpus_dir: Pathlike, the path of the data dir.
    :return: the RecodingSet and SupervisionSet for train and valid.
    """
    corpus_dir = Path(corpus_dir)
    part_path = corpus_dir / f"speech_commands_test_set_v0.0{speechcommands_version}"
    test_paths = []
    for root in os.listdir(part_path):
        if os.path.isdir(part_path / root):
            file_paths = os.listdir(part_path / root)
            test_paths += [
                os.path.join(root, file_path)
                for file_path in file_paths
                if file_path.endswith(".wav")
            ]

    test_paths = set(test_paths)

    test_recordings = []
    test_supervisions = []
    for test_path in test_paths:
        audio_path = part_path / test_path
        audio_path = audio_path.resolve()
        test_path_splits = test_path.split("/")
        audio_file_name = test_path.replace("/", "_").replace(".wav", "")
        audio_file_name_splits = test_path_splits[1].split("_")

        if test_path_splits[0] in WORDS:
            speaker = audio_file_name_splits[0]
            text = test_path_splits[0].strip()
        elif test_path_splits[0] == SILENCE:
            speaker = None
            text = ""
        elif test_path_splits[0] == UNKNOWN:
            speaker = audio_file_name_splits[1]
            text = audio_file_name_splits[0].strip()

        if not audio_path.is_file():
            logging.warning(f"No such file: {audio_path}")
            continue

        test_recording = Recording.from_file(
            path=audio_path,
            recording_id=audio_file_name,
        )
        test_recordings.append(test_recording)
        test_segment = SupervisionSegment(
            id=audio_file_name,
            recording_id=audio_file_name,
            start=0.0,
            duration=test_recording.duration,
            channel=0,
            language="English",
            speaker=speaker,
            text=text,
        )
        test_supervisions.append(test_segment)
    test_recording_set = RecordingSet.from_recordings(test_recordings)
    test_supervision_set = SupervisionSet.from_segments(test_supervisions)

    # Fix manifests
    test_recording_set, test_supervision_set = fix_manifests(
        test_recording_set, test_supervision_set
    )
    validate_recordings_and_supervisions(test_recording_set, test_supervision_set)

    return test_recording_set, test_supervision_set


def prepare_speechcommands(
    speechcommands_version: str,
    corpus_dir: Pathlike,
    output_dir: Optional[Pathlike] = None,
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
    """
    Returns the manifests which consist of the Recordings and Supervisions
    :param speechcommands_version: str, dataset version.
    :param corpus_dir: Path to the Speech Commands dataset.
    :param output_dir: Pathlike, the path where to write the manifests.
    :return: a Dict whose key is the dataset part, and the value is Dicts with the keys 'recordings' and 'supervisions'.
    """
    corpus_dir = Path(corpus_dir)

    assert corpus_dir.is_dir(), f"No such directory: {corpus_dir}"

    logging.info("Preparing Speech Commands v0.0{speechcommands_version}...")
    dataset_parts = (
        f"speech_commands_v0.0{speechcommands_version}",
        f"speech_commands_test_set_v0.0{speechcommands_version}",
    )
    subsets = _SPLITS

    if output_dir is not None:
        output_dir = Path(output_dir)
        output_dir.mkdir(parents=True, exist_ok=True)

    manifests = defaultdict(dict)

    for part in tqdm(subsets, desc="Dataset parts"):
        logging.info(
            f"Processing Speech Commands v0.0{speechcommands_version} subset: {part}"
        )
        if manifests_exist(
            part=part,
            output_dir=output_dir,
            prefix=f"speechcommands{speechcommands_version}",
            suffix="jsonl.gz",
        ):
            logging.info(
                f"Speech Commands v0.0{speechcommands_version} subset: {part} already prepared - skipping."
            )
            continue
        if part == "train":
            train_paths, valid_paths = _split_train_valid(
                speechcommands_version, corpus_dir
            )
            recording_set, supervision_set = _prepare_train(
                speechcommands_version, train_paths, corpus_dir
            )
        elif part == "valid":
            recording_set, supervision_set = _prepare_valid(
                speechcommands_version, valid_paths, corpus_dir
            )
        elif part == "test":
            recording_set, supervision_set = _prepare_test(
                speechcommands_version, corpus_dir
            )

        if output_dir is not None:
            supervision_set.to_file(
                output_dir
                / f"speechcommands{speechcommands_version}_supervisions_{part}.jsonl.gz"
            )
            recording_set.to_file(
                output_dir
                / f"speechcommands{speechcommands_version}_recordings_{part}.jsonl.gz"
            )

        manifests[part] = {"recordings": recording_set, "supervisions": supervision_set}

    return manifests

```

Name the function or class in this code that contains a `CWE-22: Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')` vulnerability.
Answer: download_speechcommands