main.py

import argparse
import queue
import multiprocessing as mp
from SoundSampler import Sampler
from soundEventUI import UI
from EventDetect import Detector


def main():
    # parser = argparse.ArgumentParser()
    # parser.add_argument('--sr', default =16000, type=int, help='sample rate')
    # parser.add_argument('--ws', default=1000, type=int, help='windows size')
    # parser.add_argument('--hs',  default=492,   type=int, help='hop size')
    # parser.add_argument('--mel', default=128,   type=int, help='mel bands')
    # parser.add_argument('--rd', default=5,    type=int, help='recording duration')
    # parser.add_argument('--frame', default=3, type=int, help='number of recording duration showing on UI')

    pmp = './model/epoch_56'
    # params for audio feature extraction (mel-spectrogram)
    parser = argparse.ArgumentParser(description='PyTorch M&ment Training using DCASE2017 Dataset')
    parser.add_argument('--dn', default='CRW_baby_cry', type=str, help='dataset name')
    parser.add_argument('--sr', default=16000, type=int, help='[fea_ext] sample rate')
    parser.add_argument('--ws', default=2000, type=int, help='[fea_ext] windows size')
    parser.add_argument('--wws', default=2048, type=int, help='[fea_ext] windows size')
    parser.add_argument('--hs', default=497, type=int, help='[fea_ext] hop size')
    parser.add_argument('--mel', default=128, type=int, help='[fea_ext] mel bands')
    parser.add_argument('--msc', default=1, type=int, help='[fea_ext] top duration of audio clip')
    parser.add_argument('--frame', default=3, type=int, help='number of recording duration showing on UI')
    parser.add_argument('--et', default=10000, type=int, help='[fea_ext] spect manti')

    # params for training
    parser.add_argument('--bs', default=32 / 4, type=int, help='[net] batch size')
    parser.add_argument('--lrde', default=30, type=int, help='[net] divided the learning rate 10 by every lrde epochs')
    parser.add_argument('--mom', default=0.9, type=float, help='[net] momentum')
    parser.add_argument('--wd', default=1e-4, type=float, help='[net] weight decay')
    parser.add_argument('--lr', default=0.01, type=float, help='[net] learning rate')
    parser.add_argument('--ep', default=31 * 3, type=int, help='[net] epoch')
    parser.add_argument('--beta', default=0.3, type=float, help='[net] hyperparameter for pre-class loss weight')
    parser.add_argument('--pmp', default=pmp, type=str, help='[net] pre-trained model path')

    args = parser.parse_args()

    audio_visual_que = queue.Queue()  # audio data container for visualization
    audio_detect_que = mp.Queue()  # audio data container for event detection
    audio_event_que = mp.Queue()  # container for event detecting results
    sampler = Sampler(audio_detect_que, audio_visual_que, args)  # sampling audio
    detector = Detector(audio_detect_que, audio_event_que, args)  # detecting audio event
    UI(audio_visual_que, audio_event_que, sampler, detector, args)


if __name__ == '__main__':
    mp.set_start_method('spawn', force=True)
    main()