[sudo]apt-get install ffmpeg lame libatlas-base-dev alsa-utils[sudo]pip3 install tensorflow

****List of CAPTURE Hardware Devices ****card1: Device [USB PnP Sound Device], device 0: USB Audio [USB Audio]  Subdevices: 0/1  Subdevice #0: subdevice #0card2: Device_1 [USB PnP Sound Device], device 0: USB Audio [USB Audio]  Subdevices: 0/1  Subdevice #0: subdevice #0

~/datasets/sound-detect/audio  -> sample_1    -> audio.mp3    -> labels.json  -> sample_2    -> audio.mp3    -> labels.json  ...

{  "00:00": "negative",  "02:13": "positive",  "04:57": "negative",  "15:41": "positive",  "18:24": "negative"}

gitclone git@github.com:/BlackLight/micmon.gitcdmicmon[sudo]pip3 install -r requirements.txt[sudo]python3 setup.py build install

micmon-datagen\    --low 250 --high 2500 --bins 100 \    --sample-duration 2 --channels 1 \    ~/datasets/sound-detect/audio  ~/datasets/sound-detect/data

importos
frommicmon.audio import AudioDirectory, AudioPlayer, AudioFilefrommicmon.dataset import DatasetWriter
basedir= os.path.expanduser('~/datasets/sound-detect')audio_dir= os.path.join(basedir, 'audio')datasets_dir= os.path.join(basedir, 'data')cutoff_frequencies= [250, 2500]
#Scan the base audio_dir for labelled audio samplesaudio_dirs= AudioDirectory.scan(audio_dir)
#Save the spectrum information and labels of the samples to a#different compressed file for each audio file.foraudio_dir in audio_dirs:    dataset_file = os.path.join(datasets_dir,os.path.basename(audio_dir.path) + '.npz')    print(f'Processing audio sample{audio_dir.path}')
    with AudioFile(audio_dir) as reader, \            DatasetWriter(dataset_file,                          low_freq=cutoff_frequencies[0],                         high_freq=cutoff_frequencies[1]) as writer:        for sample in reader:            writer += sample

importosfromtensorflow.keras import layers
frommicmon.dataset import Datasetfrommicmon.model import Model
#This is a directory that contains the saved .npz dataset filesdatasets_dir= os.path.expanduser('~/datasets/sound-detect/data')
#This is the output directory where the model will be savedmodel_dir= os.path.expanduser('~/models/sound-detect')
#This is the number of training epochs for each dataset sampleepochs= 2
#Load the datasets from the compressed files.#70% of the data points will be included in the training set,#30% of the data points will be included in the evaluation set#and used to evaluate the performance of the model.datasets= Dataset.scan(datasets_dir, validation_split=0.3)labels= ['negative', 'positive']freq_bins= len(datasets[0].samples[0])
#Create a network with 4 layers (one input layer, two intermediate layers andone output layer).#The first intermediate layer in this example will have twice the number ofunits as the number#of input units, while the second intermediate layer will have 75% of the numberof#input units. We also specify the names for the labels and the low and high frequencyrange#used when sampling.model= Model(    [        layers.Input(shape=(freq_bins,)),        layers.Dense(int(2 * freq_bins),activation='relu'),        layers.Dense(int(0.75 * freq_bins),activation='relu'),        layers.Dense(len(labels),activation='softmax'),    ],    labels=labels,    low_freq=datasets[0].low_freq,    high_freq=datasets[0].high_freq)
#Train the modelforepoch in range(epochs):    for i, dataset in enumerate(datasets):        print(f'[epoch {epoch+1}/{epochs}][audio sample {i+1}/{len(datasets)}]')        model.fit(dataset)        evaluation = model.evaluate(dataset)        print(f'Validation set loss andaccuracy: {evaluation}')
#Save the modelmodel.save(model_dir,overwrite=True)

importos
frommicmon.audio import AudioDevicefrommicmon.model import Model
model_dir= os.path.expanduser('~/models/sound-detect')model= Model.load(model_dir)audio_system= 'alsa'        # Supported: alsa andpulseaudio_device= 'plughw:2,0'  # Get list of recognizedinput devices with arecord -l
withAudioDevice(audio_system, device=audio_device) as source:    for sample in source:        source.pause()  # Pause recording while we process the frame        prediction = model.predict(sample)        print(prediction)        source.resume() #Resume recording

[sudo]apt-get install redis-server[sudo]systemctl start redis-server.service[sudo]systemctl enable redis-server.service[sudo]pip3 install 'platypush[http,pushbullet]'

backend.http:  enabled: Truepushbullet:  token: YOUR_TOKEN

#!/usr/bin/python3
importargparseimportloggingimportosimportsys
fromplatypush import RedisBusfromplatypush.message.event.custom import CustomEvent
frommicmon.audio import AudioDevicefrommicmon.model import Model
logger= logging.getLogger('micmon')

defget_args():    parser = argparse.ArgumentParser()    parser.add_argument('model_path',help='Path to the file/directory containing the saved Tensorflow model')    parser.add_argument('-i', help='Input sounddevice (e.g. hw:0,1 or default)', required=True, dest='sound_device')    parser.add_argument('-e', help='Name of theevent that should be raised when a positive event occurs', required=True,dest='event_type')    parser.add_argument('-s', '--sound-server',help='Sound server to be used (available: alsa, pulse)', required=False,default='alsa', dest='sound_server')    parser.add_argument('-P','--positive-label', help='Model output label name/index to indicate a positivesample (default: positive)', required=False, default='positive',dest='positive_label')    parser.add_argument('-N','--negative-label', help='Model output label name/index to indicate a negativesample (default: negative)', required=False, default='negative',dest='negative_label')    parser.add_argument('-l','--sample-duration', help='Length of the FFT audio samples (default: 2seconds)', required=False, type=float, default=2., dest='sample_duration')    parser.add_argument('-r', '--sample-rate',help='Sample rate (default: 44100 Hz)', required=False, type=int,default=44100, dest='sample_rate')    parser.add_argument('-c', '--channels',help='Number of audio recording channels (default: 1)', required=False,type=int, default=1, dest='channels')    parser.add_argument('-f', '--ffmpeg-bin',help='FFmpeg executable path (default: ffmpeg)', required=False,default='ffmpeg', dest='ffmpeg_bin')    parser.add_argument('-v', '--verbose',help='Verbose/debug mode', required=False, action='store_true', dest='debug')    parser.add_argument('-w','--window-duration', help='Duration of the look-back window (default: 10seconds)', required=False, type=float, default=10., dest='window_length')    parser.add_argument('-n','--positive-samples', help='Number of positive samples detected over the windowduration to trigger the event (default: 1)', required=False, type=int,default=1, dest='positive_samples')
    opts, args =parser.parse_known_args(sys.argv[1:])    return opts

defmain():    args = get_args()    if args.debug:        logger.setLevel(logging.DEBUG)
    model_dir =os.path.abspath(os.path.expanduser(args.model_path))    model = Model.load(model_dir)    window = []    cur_prediction = args.negative_label    bus = RedisBus()
    with AudioDevice(system=args.sound_server,                     device=args.sound_device,                    sample_duration=args.sample_duration,                    sample_rate=args.sample_rate,                     channels=args.channels,                    ffmpeg_bin=args.ffmpeg_bin,                     debug=args.debug) assource:        for sample in source:            source.pause()  # Pause recording while we process the frame            prediction = model.predict(sample)            logger.debug(f'Sample prediction:{prediction}')            has_change = False
            if len(window) < args.window_length:                window += [prediction]            else:                window = window[1:] +[prediction]
            positive_samples = len([pred forpred in window if pred == args.positive_label])            if args.positive_samples <=positive_samples and \                    prediction ==args.positive_label and \                    cur_prediction !=args.positive_label:                cur_prediction =args.positive_label                has_change = True                logging.info(f'Positive samplethreshold detected ({positive_samples}/{len(window)})')            elif args.positive_samples >positive_samples and \                    prediction ==args.negative_label and \                    cur_prediction !=args.negative_label:                cur_prediction = args.negative_label                has_change = True                logging.info(f'Negative samplethreshold detected ({len(window)-positive_samples}/{len(window)})')
            if has_change:                evt = CustomEvent(subtype=args.event_type,state=prediction)                bus.post(evt)
            source.resume() # Resume recording

if__name__ == '__main__':    main()

mkdir-p ~/.config/platypush/scriptscd~/.config/platypush/scripts#Define the directory as a moduletouch__init__.py#Create a script for the baby-cry eventsvibabymonitor.py
babymonitor.py的内容如下：
fromplatypush.context import get_pluginfromplatypush.event.hook import hookfromplatypush.message.event.custom import CustomEvent @hook(CustomEvent,subtype='baby-cry', state='positive')defon_baby_cry_start(event, **_):    pb = get_plugin('pushbullet')    pb.send_note(title='Baby cry status',body='The baby is crying!') @hook(CustomEvent,subtype='baby-cry', state='negative')defon_baby_cry_stop(event, **_):    pb = get_plugin('pushbullet')    pb.send_note(title='Baby cry status',body='The baby stopped crying - good job!')

mkdir-p ~/.config/systemd/userwget-O ~/.config/systemd/user/platypush.service \    https://raw.githubusercontent.com/BlackLight/platypush/master/examples/systemd/platypush.servicesystemctl--user start platypush.servicesystemctl--user enable platypush.service

~/.config/systemd/user/babymonitor.service

[Unit]Description=Monitorto detect my baby's criesAfter=network.targetsound.target[Service]ExecStart=/home/pi/bin/micmon_detect.py-i plughw:2,0 -e baby-cry -w 10 -n 2 ~/models/sound-detectRestart=alwaysRestartSec=10[Install]WantedBy=default.target

systemctl--user start babymonitor.servicesystemctl--user enable babymonitor.service

[sudo]pip3 install 'platypush[http,camera,picamera]'

camera.pi:    listen_port: 5001

wgethttp://raspberry-pi:8008/camera/pi/photo.jpg

http://raspberry-pi:8008/camera/pi/video.mjpg

mkdir-p ~/.config/platypush/scriptscd~/.config/platypush/scriptstouch__init__.pyvicamera.py 

fromplatypush.context import get_pluginfromplatypush.event.hook import hookfromplatypush.message.event.application import ApplicationStartedEvent
@hook(ApplicationStartedEvent)defon_application_started(event, **_):    cam = get_plugin('camera.pi')    cam.start_streaming()

vlctcp/h264://raspberry-pi:5001