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# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# USAGE: python get_hi-mia_data.py --data_root=<where to put data>
import argparse
import json
import logging as _logging
import os
import tarfile
import urllib.request
from glob import glob
import librosa as l
from sklearn.model_selection import StratifiedShuffleSplit
from tqdm import tqdm
parser = argparse.ArgumentParser(description="HI-MIA Data download")
parser.add_argument("--data_root", required=True, default=None, type=str)
parser.add_argument("--log_level", default=20, type=int)
args = parser.parse_args()
logging = _logging.getLogger(__name__)
logging.addHandler(_logging.StreamHandler())
logging.setLevel(args.log_level)
URL = {
"dev": "http://www.openslr.org/resources/85/dev.tar.gz",
"test": "http://www.openslr.org/resources/85/test.tar.gz",
"train": "http://www.openslr.org/resources/85/train.tar.gz",
}
def __retrieve_with_progress(source: str, filename: str):
"""
Downloads source to destination
Displays progress bar
Args:
source: url of resource
destination: local filepath
Returns:
"""
with open(filename, "wb") as f:
response = urllib.request.urlopen(source)
total = response.length
if total is None:
f.write(response.content)
else:
with tqdm(total=total, unit="B", unit_scale=True, unit_divisor=1024) as pbar:
for data in response:
f.write(data)
pbar.update(len(data))
def __maybe_download_file(destination: str, source: str):
"""
Downloads source to destination if it doesn't exist.
If exists, skips download
Args:
destination: local filepath
source: url of resource
Returns:
"""
source = URL[source]
if not os.path.exists(destination) and not os.path.exists(os.path.splitext(destination)[0]):
logging.info("{0} does not exist. Downloading ...".format(destination))
__retrieve_with_progress(source, filename=destination + ".tmp")
os.rename(destination + ".tmp", destination)
logging.info("Downloaded {0}.".format(destination))
elif os.path.exists(destination):
logging.info("Destination {0} exists. Skipping.".format(destination))
elif os.path.exists(os.path.splitext(destination)[0]):
logging.warning(
"Assuming extracted folder %s contains the extracted files from %s. Will not download.",
os.path.basename(destination),
destination,
)
return destination
def __extract_all_files(filepath: str, data_root: str, data_dir: str):
if not os.path.exists(data_dir):
extract_file(filepath, data_root)
audio_dir = os.path.join(data_dir, "wav")
for subfolder, _, filelist in os.walk(audio_dir):
for ftar in filelist:
extract_file(os.path.join(subfolder, ftar), subfolder)
else:
logging.info("Skipping extracting. Data already there %s" % data_dir)
def extract_file(filepath: str, data_dir: str):
try:
tar = tarfile.open(filepath, encoding='utf-8')
tar.extractall(data_dir)
tar.close()
except Exception:
logging.info("Not extracting. Maybe already there?")
def __remove_tarred_files(filepath: str, data_dir: str):
if os.path.exists(data_dir) and os.path.isfile(filepath):
logging.info("Deleting %s" % filepath)
os.remove(filepath)
def write_file(name, lines, idx):
with open(name, "w") as fout:
for i in idx:
dic = lines[i]
json.dump(dic, fout)
fout.write("\n")
logging.info("wrote %s", name)
def __process_data(data_folder: str, data_set: str):
"""
To generate manifest
Args:
data_folder: source with wav files
Returns:
"""
fullpath = os.path.abspath(data_folder)
filelist = glob(fullpath + "/**/*.wav", recursive=True)
out = os.path.join(fullpath, data_set + "_all.json")
utt2spk = os.path.join(fullpath, "utt2spk")
utt2spk_file = open(utt2spk, "w")
id = -2 # speaker id
if os.path.exists(out):
logging.warning(
"%s already exists and is assumed to be processed. If not, please delete %s and rerun this script",
out,
out,
)
return
speakers = []
lines = []
with open(out, "w") as outfile:
for line in tqdm(filelist):
line = line.strip()
y, sr = l.load(line, sr=None)
if sr != 16000:
y, sr = l.load(line, sr=16000)
l.output.write_wav(line, y, sr)
dur = l.get_duration(y=y, sr=sr)
if data_set == "test":
speaker = line.split("/")[-1].split(".")[0].split("_")[0]
else:
speaker = line.split("/")[id]
speaker = list(speaker)
speaker = "".join(speaker)
speakers.append(speaker)
meta = {"audio_filepath": line, "duration": float(dur), "label": speaker}
lines.append(meta)
json.dump(meta, outfile)
outfile.write("\n")
utt2spk_file.write(line.split("/")[-1] + "\t" + speaker + "\n")
utt2spk_file.close()
if data_set != "test":
sss = StratifiedShuffleSplit(n_splits=1, test_size=0.1, random_state=42)
for train_idx, test_idx in sss.split(speakers, speakers):
print(len(train_idx))
out = os.path.join(fullpath, "train.json")
write_file(out, lines, train_idx)
out = os.path.join(fullpath, "dev.json")
write_file(out, lines, test_idx)
def main():
data_root = args.data_root
for data_set in URL.keys():
# data_set = 'data_aishell'
logging.info("\n\nWorking on: {0}".format(data_set))
file_path = os.path.join(data_root, data_set + ".tgz")
logging.info("Getting {0}".format(data_set))
__maybe_download_file(file_path, data_set)
logging.info("Extracting {0}".format(data_set))
data_folder = os.path.join(data_root, data_set)
__extract_all_files(file_path, data_root, data_folder)
__remove_tarred_files(file_path, data_folder)
logging.info("Processing {0}".format(data_set))
__process_data(data_folder, data_set)
logging.info("Done!")
if __name__ == "__main__":
main()
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