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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.
import os
from argparse import ArgumentParser
import numpy as np
from nemo.collections.asr.parts.utils.vad_utils import prepare_manifest
from nemo.utils import logging
"""
This script is designed for inference of frame level Voice Activity Detection (VAD)
This script serves three goals:
(1) Write audio files to manifest
(2) Split audio file for avoiding CUDA memory issue
(3) Take care of joint of seperate json line for an audio file
Usage:
python write_long_audio_manifest.py --inp_dir=<FULL PATH OF FOLDER OF AUDIO FILES> --split_duration=300 --window_length_in_sec=0.63 --num_worker=10
"""
def main():
parser = ArgumentParser()
parser.add_argument("--inp_dir", type=str, required=True, help="(full path) folder of files to be processed")
parser.add_argument(
"--inp_list", type=str, help="(full path) a file contains NAME of files inside inp_dir to be processed"
)
parser.add_argument("--out_dir", type=str, default=".", help="(full path) location to store generated json file")
parser.add_argument("--manifest_name", type=str, default="generated_manifest", help="name of generated json file")
parser.add_argument("--split_duration", type=int, required=True, help="max duration of each audio clip/line")
parser.add_argument(
"--window_length_in_sec",
type=float,
default=0.63,
help="window length in sec for VAD context input , default is 0.63s",
)
parser.add_argument("--num_workers", type=int, default=4, help="number of workers for multiprocessing")
args = parser.parse_args()
if not args.inp_list:
input_audios = []
for root, dirs, files in os.walk(args.inp_dir):
for basename in files:
if basename.endswith('.wav'):
filename = os.path.join(root, basename)
input_audios.append(filename)
else:
name_list = np.loadtxt(args.inp_list, dtype='str')
input_audios = [os.path.join(args.inp_dir, name + ".wav") for name in name_list]
input_list = []
for i in input_audios:
input_list.append({'audio_filepath': i, "offset": 0, "duration": None})
logging.info(f"Number of wav files to be processed: {len(input_audios)}")
output_path = os.path.join(args.out_dir, args.manifest_name + '.json')
logging.info("Split long audio file to avoid CUDA memory issue")
logging.debug("Try smaller split_duration if you still have CUDA memory issue")
config = {
'input': input_list,
'window_length_in_sec': args.window_length_in_sec,
'split_duration': args.split_duration,
'num_workers': args.num_workers,
'prepared_manfiest_vad_input': output_path,
}
manifest_vad_input = prepare_manifest(config)
logging.info(f"Done! Save to {manifest_vad_input}")
if __name__ == '__main__':
main()
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