content: import numpy as np
  import as_classification.ann_models
  import as_sound.features.extractFeatures as ef
  import as_classification.utilities
  import os
  import matplotlib.pyplot as plt
  
  
  # -------------------------------
  # CREATE MODEL
  # -------------------------------
  
  model = as_classification.ann_models.ANN_5FCL()
  model.initialize(15,2)
  
  # -------------------------------
  # READ AUDIO FILES
  # -------------------------------
  
  speech_data = ef.computeSupervectorForFile(os.path.dirname(os.path.realpath(__file__)) + '/data/Speech.wav', 8000, 2048, 2049)
  noise_data = ef.computeSupervectorForFile(os.path.dirname(os.path.realpath(__file__)) + '/data/Noise.wav', 8000, 2048, 2049)
  
  whole_data = np.hstack((speech_data, noise_data))
  whole_data = np.swapaxes(whole_data,0,1)
  whole_labels = np.zeros((whole_data.shape[0], 2))
  whole_labels[:speech_data.shape[1],0] = 1
  whole_labels[speech_data.shape[1]:,1] = 1
  
  training_data = {"labels": whole_labels,
                   "data": whole_data}
  
  
  training_data, test_data = as_classification.utilities.divideTrainingData(training_data, 0.6)
  
  
  # -------------------------------
  # TRAIN
  # -------------------------------
  
  model.train(training_data, test_data, 10, 20, 100)
  
  model.saveCheckpoint(os.path.dirname(os.path.realpath(__file__)) + '/data/vadModel_ANN_5FCL.chkp')
  
  
  
  
  
  
  #xp = np.arange(0,prediction.shape[0])
  
  #plt.plot(xp, test_data[:,14], '-b', label='RMS')
  #plt.plot(xp, prediction[:,0], '-r', label='ANN Output')
  #plt.legend(loc='upper left')
  
  #plt.show()
  
  #feat = as_sound.features.extractFeatures.computeSupervector(normalized_data)
  
  
  