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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_aishell_data.py --data_root=<where to put data>
import argparse
import json
import logging
import os
import subprocess
import tarfile
import urllib.request
from tqdm import tqdm
parser = argparse.ArgumentParser(description="Aishell Data download")
parser.add_argument("--data_root", required=True, default=None, type=str)
args = parser.parse_args()
URL = {"data_aishell": "http://www.openslr.org/resources/33/data_aishell.tgz"}
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):
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_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)
tar.extractall(data_dir)
tar.close()
except Exception:
logging.info("Not extracting. Maybe already there?")
def __process_data(data_folder: str, dst_folder: str):
"""
To generate manifest
Args:
data_folder: source with wav files
dst_folder: where manifest files will be stored
Returns:
"""
if not os.path.exists(dst_folder):
os.makedirs(dst_folder)
transcript_file = os.path.join(data_folder, "transcript", "aishell_transcript_v0.8.txt")
transcript_dict = {}
with open(transcript_file, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
audio_id, text = line.split(" ", 1)
# remove white space
text = text.replace(" ", "")
transcript_dict[audio_id] = text
data_types = ["train", "dev", "test"]
vocab_count = {}
for dt in data_types:
json_lines = []
audio_dir = os.path.join(data_folder, "wav", dt)
for sub_folder, _, file_list in os.walk(audio_dir):
for fname in file_list:
audio_path = os.path.join(sub_folder, fname)
audio_id = fname.strip(".wav")
if audio_id not in transcript_dict:
continue
text = transcript_dict[audio_id]
for li in text:
vocab_count[li] = vocab_count.get(li, 0) + 1
duration = subprocess.check_output("soxi -D {0}".format(audio_path), shell=True)
duration = float(duration)
json_lines.append(
json.dumps(
{"audio_filepath": os.path.abspath(audio_path), "duration": duration, "text": text,},
ensure_ascii=False,
)
)
manifest_path = os.path.join(dst_folder, dt + ".json")
with open(manifest_path, "w", encoding="utf-8") as fout:
for line in json_lines:
fout.write(line + "\n")
vocab = sorted(vocab_count.items(), key=lambda k: k[1], reverse=True)
vocab_file = os.path.join(dst_folder, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as f:
for v, c in vocab:
f.write(v + "\n")
def main():
data_root = args.data_root
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)
logging.info("Processing {0}".format(data_set))
__process_data(data_folder, data_folder)
logging.info("Done!")
if __name__ == "__main__":
main()
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