Source code for torchaudio.datasets.speechcommands
import os
from pathlib import Path
from typing import Optional, Tuple, Union
from torch import Tensor
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import _load_waveform, extract_archive
FOLDER_IN_ARCHIVE = "SpeechCommands"
URL = "speech_commands_v0.02"
HASH_DIVIDER = "_nohash_"
EXCEPT_FOLDER = "_background_noise_"
SAMPLE_RATE = 16000
_CHECKSUMS = {
"http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz": "743935421bb51cccdb6bdd152e04c5c70274e935c82119ad7faeec31780d811d", # noqa: E501
"http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz": "af14739ee7dc311471de98f5f9d2c9191b18aedfe957f4a6ff791c709868ff58", # noqa: E501
}
def _load_list(root, *filenames):
output = []
for filename in filenames:
filepath = os.path.join(root, filename)
with open(filepath) as fileobj:
output += [os.path.normpath(os.path.join(root, line.strip())) for line in fileobj]
return output
def _get_speechcommands_metadata(filepath: str, path: str) -> Tuple[str, int, str, str, int]:
relpath = os.path.relpath(filepath, path)
reldir, filename = os.path.split(relpath)
_, label = os.path.split(reldir)
# Besides the officially supported split method for datasets defined by "validation_list.txt"
# and "testing_list.txt" over "speech_commands_v0.0x.tar.gz" archives, an alternative split
# method referred to in paragraph 2-3 of Section 7.1, references 13 and 14 of the original
# paper, and the checksums file from the tensorflow_datasets package [1] is also supported.
# Some filenames in those "speech_commands_test_set_v0.0x.tar.gz" archives have the form
# "xxx.wav.wav", so file extensions twice needs to be stripped twice.
# [1] https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/url_checksums/speech_commands.txt
speaker, _ = os.path.splitext(filename)
speaker, _ = os.path.splitext(speaker)
speaker_id, utterance_number = speaker.split(HASH_DIVIDER)
utterance_number = int(utterance_number)
return relpath, SAMPLE_RATE, label, speaker_id, utterance_number
[docs]class SPEECHCOMMANDS(Dataset):
"""*Speech Commands* :cite:`speechcommandsv2` 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 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``).
subset (str or None, optional):
Select a subset of the dataset [None, "training", "validation", "testing"]. None means
the whole dataset. "validation" and "testing" are defined in "validation_list.txt" and
"testing_list.txt", respectively, and "training" is the rest. Details for the files
"validation_list.txt" and "testing_list.txt" are explained in the README of the dataset
and in the introduction of Section 7 of the original paper and its reference 12. The
original paper can be found `here <https://arxiv.org/pdf/1804.03209.pdf>`_. (Default: ``None``)
"""
def __init__(
self,
root: Union[str, Path],
url: str = URL,
folder_in_archive: str = FOLDER_IN_ARCHIVE,
download: bool = False,
subset: Optional[str] = None,
) -> None:
if subset is not None and subset not in ["training", "validation", "testing"]:
raise ValueError("When `subset` is not None, it must be one of ['training', 'validation', 'testing'].")
if url in [
"speech_commands_v0.01",
"speech_commands_v0.02",
]:
base_url = "http://download.tensorflow.org/data/"
ext_archive = ".tar.gz"
url = os.path.join(base_url, url + ext_archive)
# Get string representation of 'root' in case Path object is passed
root = os.fspath(root)
self._archive = os.path.join(root, folder_in_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_to_file(url, archive, hash_prefix=checksum)
extract_archive(archive, self._path)
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"
)
if subset == "validation":
self._walker = _load_list(self._path, "validation_list.txt")
elif subset == "testing":
self._walker = _load_list(self._path, "testing_list.txt")
elif subset == "training":
excludes = set(_load_list(self._path, "validation_list.txt", "testing_list.txt"))
walker = sorted(str(p) for p in Path(self._path).glob("*/*.wav"))
self._walker = [
w
for w in walker
if HASH_DIVIDER in w and EXCEPT_FOLDER not in w and os.path.normpath(w) not in excludes
]
else:
walker = sorted(str(p) for p in Path(self._path).glob("*/*.wav"))
self._walker = [w for w in walker if HASH_DIVIDER in w and EXCEPT_FOLDER not in w]
[docs] def get_metadata(self, n: int) -> Tuple[str, int, str, str, int]:
"""Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
but otherwise returns the same fields as :py:func:`__getitem__`.
Args:
n (int): The index of the sample to be loaded
Returns:
Tuple of the following items;
str:
Path to the audio
int:
Sample rate
str:
Label
str:
Speaker ID
int:
Utterance number
"""
fileid = self._walker[n]
return _get_speechcommands_metadata(fileid, self._archive)
[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 of the following items;
Tensor:
Waveform
int:
Sample rate
str:
Label
str:
Speaker ID
int:
Utterance number
"""
metadata = self.get_metadata(n)
waveform = _load_waveform(self._archive, metadata[0], metadata[1])
return (waveform,) + metadata[1:]
def __len__(self) -> int:
return len(self._walker)
