new_contents: from models.cuberun import CubeRun
  import numpy as np
  import utils
  
  def get_model():
      nb_classes = 19
      input_shape = (257, 624, 1)
  
      model = CubeRun(nb_classes=nb_classes, input_shape=input_shape)
  
      model.load_weights("../weights/2016_11_16_06:31:03_cuberun.h5")
  
      return model
  
  def predict(model, filename):
      fs, x = utils.read_wave_file("../datasets/mlsp2013/test/"+filename+".wav")
  
      (f, t, Sxx) = utils.wave_to_spectrogram(x, fs)
  
      X = np.array([Sxx])
  
      X = X.reshape(X.shape[0], 257, 624, 1)
  
      y = model.predict(X, batch_size=32, verbose=1)
      y = np.round(y)
  
      return [binary_to_id(v) for v in  y]
  
  def binary_to_id(Y):
      i = 0
      r = []
      for y in Y:
          if y == 1:
              r.append(i)
          i = i+1
  
      return r
  
subject: Add a first draft of the prediction module.