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91 lines
3.4 KiB
Python
91 lines
3.4 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from argparse import ArgumentParser
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import numpy as np
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from nemo.collections.asr.parts.utils.vad_utils import prepare_manifest
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from nemo.utils import logging
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"""
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This script is designed for inference of frame level Voice Activity Detection (VAD)
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This script serves three goals:
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(1) Write audio files to manifest
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(2) Split audio file for avoiding CUDA memory issue
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(3) Take care of joint of seperate json line for an audio file
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Usage:
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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
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"""
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def main():
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parser = ArgumentParser()
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parser.add_argument("--inp_dir", type=str, required=True, help="(full path) folder of files to be processed")
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parser.add_argument(
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"--inp_list", type=str, help="(full path) a file contains NAME of files inside inp_dir to be processed"
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)
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parser.add_argument("--out_dir", type=str, default=".", help="(full path) location to store generated json file")
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parser.add_argument("--manifest_name", type=str, default="generated_manifest", help="name of generated json file")
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parser.add_argument("--split_duration", type=int, required=True, help="max duration of each audio clip/line")
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parser.add_argument(
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"--window_length_in_sec",
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type=float,
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default=0.63,
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help="window length in sec for VAD context input , default is 0.63s",
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)
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parser.add_argument("--num_workers", type=int, default=4, help="number of workers for multiprocessing")
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args = parser.parse_args()
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if not args.inp_list:
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input_audios = []
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for root, dirs, files in os.walk(args.inp_dir):
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for basename in files:
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if basename.endswith('.wav'):
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filename = os.path.join(root, basename)
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input_audios.append(filename)
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else:
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name_list = np.loadtxt(args.inp_list, dtype='str')
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input_audios = [os.path.join(args.inp_dir, name + ".wav") for name in name_list]
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input_list = []
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for i in input_audios:
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input_list.append({'audio_filepath': i, "offset": 0, "duration": None})
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logging.info(f"Number of wav files to be processed: {len(input_audios)}")
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output_path = os.path.join(args.out_dir, args.manifest_name + '.json')
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logging.info("Split long audio file to avoid CUDA memory issue")
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logging.debug("Try smaller split_duration if you still have CUDA memory issue")
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config = {
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'input': input_list,
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'window_length_in_sec': args.window_length_in_sec,
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'split_duration': args.split_duration,
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'num_workers': args.num_workers,
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'prepared_manfiest_vad_input': output_path,
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}
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manifest_vad_input = prepare_manifest(config)
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logging.info(f"Done! Save to {manifest_vad_input}")
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if __name__ == '__main__':
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main()
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