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)