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# Copyright (c) 2020, 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_librispeech_data.py --data_root=<where to put data>
# --data_set=<datasets_to_download> --num_workers=<number of parallel workers>
# where <datasets_to_download> can be: dev_clean, dev_other, test_clean,
# test_other, train_clean_100, train_clean_360, train_other_500 or ALL
# You can also put more than one data_set comma-separated:
# --data_set=dev_clean,train_clean_100
import argparse
import fnmatch
import functools
import json
import logging
import multiprocessing
import os
import subprocess
import tarfile
import urllib.request
from sox import Transformer
from tqdm import tqdm
parser = argparse.ArgumentParser(description="LibriSpeech Data download")
parser.add_argument("--data_root", required=True, default=None, type=str)
parser.add_argument("--data_sets", default="dev_clean", type=str)
parser.add_argument("--num_workers", default=4, type=int)
parser.add_argument("--log", dest="log", action="store_true", default=False)
args = parser.parse_args()
URLS = {
"TRAIN_CLEAN_100": ("http://www.openslr.org/resources/12/train-clean-100.tar.gz"),
"TRAIN_CLEAN_360": ("http://www.openslr.org/resources/12/train-clean-360.tar.gz"),
"TRAIN_OTHER_500": ("http://www.openslr.org/resources/12/train-other-500.tar.gz"),
"DEV_CLEAN": "http://www.openslr.org/resources/12/dev-clean.tar.gz",
"DEV_OTHER": "http://www.openslr.org/resources/12/dev-other.tar.gz",
"TEST_CLEAN": "http://www.openslr.org/resources/12/test-clean.tar.gz",
"TEST_OTHER": "http://www.openslr.org/resources/12/test-other.tar.gz",
"DEV_CLEAN_2": "https://www.openslr.org/resources/31/dev-clean-2.tar.gz",
"TRAIN_CLEAN_5": "https://www.openslr.org/resources/31/train-clean-5.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 = URLS[source]
if not os.path.exists(destination):
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))
else:
logging.info("Destination {0} exists. Skipping.".format(destination))
return destination
def __extract_file(filepath: str, data_dir: str):
try:
tar = tarfile.open(filepath)
tar.extractall(data_dir)
tar.close()
except Exception:
logging.info("Not extracting. Maybe already there?")
def __process_transcript(file_path: str, dst_folder: str):
"""
Converts flac files to wav from a given transcript, capturing the metadata.
Args:
file_path: path to a source transcript with flac sources
dst_folder: path where wav files will be stored
Returns:
a list of metadata entries for processed files.
"""
entries = []
root = os.path.dirname(file_path)
with open(file_path, encoding="utf-8") as fin:
for line in fin:
id, text = line[: line.index(" ")], line[line.index(" ") + 1 :]
transcript_text = text.lower().strip()
# Convert FLAC file to WAV
flac_file = os.path.join(root, id + ".flac")
wav_file = os.path.join(dst_folder, id + ".wav")
if not os.path.exists(wav_file):
Transformer().build(flac_file, wav_file)
# check duration
duration = subprocess.check_output("soxi -D {0}".format(wav_file), shell=True)
entry = {}
entry["audio_filepath"] = os.path.abspath(wav_file)
entry["duration"] = float(duration)
entry["text"] = transcript_text
entries.append(entry)
return entries
def __process_data(data_folder: str, dst_folder: str, manifest_file: str, num_workers: int):
"""
Converts flac to wav and build manifests's json
Args:
data_folder: source with flac files
dst_folder: where wav files will be stored
manifest_file: where to store manifest
num_workers: number of parallel workers processing files
Returns:
"""
if not os.path.exists(dst_folder):
os.makedirs(dst_folder)
files = []
entries = []
for root, dirnames, filenames in os.walk(data_folder):
for filename in fnmatch.filter(filenames, "*.trans.txt"):
files.append(os.path.join(root, filename))
with multiprocessing.Pool(num_workers) as p:
processing_func = functools.partial(__process_transcript, dst_folder=dst_folder)
results = p.imap(processing_func, files)
for result in tqdm(results, total=len(files)):
entries.extend(result)
with open(manifest_file, "w") as fout:
for m in entries:
fout.write(json.dumps(m) + "\n")
def main():
data_root = args.data_root
data_sets = args.data_sets
num_workers = args.num_workers
if args.log:
logging.basicConfig(level=logging.INFO)
if data_sets == "ALL":
data_sets = "dev_clean,dev_other,train_clean_100,train_clean_360,train_other_500,test_clean,test_other"
if data_sets == "mini":
data_sets = "dev_clean_2,train_clean_5"
for data_set in data_sets.split(","):
logging.info("\n\nWorking on: {0}".format(data_set))
filepath = os.path.join(data_root, data_set + ".tar.gz")
logging.info("Getting {0}".format(data_set))
__maybe_download_file(filepath, data_set.upper())
logging.info("Extracting {0}".format(data_set))
__extract_file(filepath, data_root)
logging.info("Processing {0}".format(data_set))
__process_data(
os.path.join(os.path.join(data_root, "LibriSpeech"), data_set.replace("_", "-"),),
os.path.join(os.path.join(data_root, "LibriSpeech"), data_set.replace("_", "-"),) + "-processed",
os.path.join(data_root, data_set + ".json"),
num_workers=num_workers,
)
logging.info("Done!")
if __name__ == "__main__":
main()
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