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run.py
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run.py
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import time
import numpy as np
from utils import *
from scipy.io import wavfile
from keras.models import load_model
import soundfile as sf
import sounddevice as sd
def main():
chime, fs = sf.read("audios/chime.wav", dtype='float32')
model = load_model("models/trigger word detection model.h5")
###create the pre interfence :-
audio_arr = np.zeros((441000, ))
audio_arr = np.int16(audio_arr / np.max(np.abs(audio_arr)) * 32767)
spec_arr = rec_to_spec(audio_arr).swapaxes(0, 1)
model.predict(np.expand_dims(spec_arr, axis = 0))
while True:
try:
rate, new_aud_arr = wavfile.read("audios/current_recording.wav")
except:
time.sleep(0.05)
continue
new_spec_arr = rec_to_spec(new_aud_arr).swapaxes(0, 1)
spec_arr = np.vstack([spec_arr, new_spec_arr])[-5511:, :]
prediction = model.predict(np.expand_dims(spec_arr, axis = 0))
if has_new_triggerword(np.squeeze(prediction, axis = 0), 2.5, 10, 0.5)==True:
sd.play(chime, fs)
time.sleep(2.6)
if __name__=="__main__":
main()