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init
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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import List, Tuple
import csv
import errno
import hashlib
import logging
import os
import sys
import tarfile
import threading
import zipfile
from _io import TextIOWrapper
from queue import Queue
from typing import Any, Iterable, List, Optional, Tuple, Union
import torch
import urllib
import urllib.request
from torch.utils.data import Dataset
from torch.utils.model_zoo import tqdm
def get_audio_files(manifest_path: str) -> Tuple[str, List[str], List[int]]:
fnames, sizes = [], []
with open(manifest_path, "r") as f:
root_dir = f.readline().strip()
for line in f:
items = line.strip().split("\t")
assert (
len(items) == 2
), f"File must have two columns separated by tab. Got {line}"
fnames.append(items[0])
sizes.append(int(items[1]))
return root_dir, fnames, sizes
def download_url(
url: str,
download_folder: str,
filename: Optional[str] = None,
hash_value: Optional[str] = None,
hash_type: str = "sha256",
progress_bar: bool = True,
resume: bool = False,
) -> None:
"""Download file to disk.
Args:
url (str): Url.
download_folder (str): Folder to download file.
filename (str, optional): Name of downloaded file. If None, it is inferred from the url (Default: ``None``).
hash_value (str, optional): Hash for url (Default: ``None``).
hash_type (str, optional): Hash type, among "sha256" and "md5" (Default: ``"sha256"``).
progress_bar (bool, optional): Display a progress bar (Default: ``True``).
resume (bool, optional): Enable resuming download (Default: ``False``).
"""
req = urllib.request.Request(url, method="HEAD")
req_info = urllib.request.urlopen(req).info()
# Detect filename
filename = filename or req_info.get_filename() or os.path.basename(url)
filepath = os.path.join(download_folder, filename)
if resume and os.path.exists(filepath):
mode = "ab"
local_size: Optional[int] = os.path.getsize(filepath)
elif not resume and os.path.exists(filepath):
raise RuntimeError(
"{} already exists. Delete the file manually and retry.".format(filepath)
)
else:
mode = "wb"
local_size = None
if hash_value and local_size == int(req_info.get("Content-Length", -1)):
with open(filepath, "rb") as file_obj:
if validate_file(file_obj, hash_value, hash_type):
return
raise RuntimeError(
"The hash of {} does not match. Delete the file manually and retry.".format(
filepath
)
)
with open(filepath, mode) as fpointer:
for chunk in stream_url(url, start_byte=local_size, progress_bar=progress_bar):
fpointer.write(chunk)
with open(filepath, "rb") as file_obj:
if hash_value and not validate_file(file_obj, hash_value, hash_type):
raise RuntimeError(
"The hash of {} does not match. Delete the file manually and retry.".format(
filepath
)
)
def validate_file(file_obj: Any, hash_value: str, hash_type: str = "sha256") -> bool:
"""Validate a given file object with its hash.
Args:
file_obj: File object to read from.
hash_value (str): Hash for url.
hash_type (str, optional): Hash type, among "sha256" and "md5" (Default: ``"sha256"``).
Returns:
bool: return True if its a valid file, else False.
"""
if hash_type == "sha256":
hash_func = hashlib.sha256()
elif hash_type == "md5":
hash_func = hashlib.md5()
else:
raise ValueError
while True:
# Read by chunk to avoid filling memory
chunk = file_obj.read(1024**2)
if not chunk:
break
hash_func.update(chunk)
return hash_func.hexdigest() == hash_value
def extract_archive(
from_path: str, to_path: Optional[str] = None, overwrite: bool = False
) -> List[str]:
"""Extract archive.
Args:
from_path (str): the path of the archive.
to_path (str, optional): the root path of the extraced files (directory of from_path) (Default: ``None``)
overwrite (bool, optional): overwrite existing files (Default: ``False``)
Returns:
list: List of paths to extracted files even if not overwritten.
Examples:
>>> url = 'http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/validation.tar.gz'
>>> from_path = './validation.tar.gz'
>>> to_path = './'
>>> torchaudio.datasets.utils.download_from_url(url, from_path)
>>> torchaudio.datasets.utils.extract_archive(from_path, to_path)
"""
if to_path is None:
to_path = os.path.dirname(from_path)
try:
with tarfile.open(from_path, "r") as tar:
logging.info("Opened tar file {}.".format(from_path))
files = []
for file_ in tar: # type: Any
file_path = os.path.join(to_path, file_.name)
if file_.isfile():
files.append(file_path)
if os.path.exists(file_path):
logging.info("{} already extracted.".format(file_path))
if not overwrite:
continue
tar.extract(file_, to_path)
return files
except tarfile.ReadError:
pass
try:
with zipfile.ZipFile(from_path, "r") as zfile:
logging.info("Opened zip file {}.".format(from_path))
files = zfile.namelist()
for file_ in files:
file_path = os.path.join(to_path, file_)
if os.path.exists(file_path):
logging.info("{} already extracted.".format(file_path))
if not overwrite:
continue
zfile.extract(file_, to_path)
return files
except zipfile.BadZipFile:
pass
raise NotImplementedError("We currently only support tar.gz, tgz, and zip achives.")