text: #!/usr/bin/python3
  '''Copyright (c) 2018 Mozilla
  
     Redistribution and use in source and binary forms, with or without
     modification, are permitted provided that the following conditions
     are met:
  
     - Redistributions of source code must retain the above copyright
     notice, this list of conditions and the following disclaimer.
  
     - Redistributions in binary form must reproduce the above copyright
     notice, this list of conditions and the following disclaimer in the
     documentation and/or other materials provided with the distribution.
  
     THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
     ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
     LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
     A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE FOUNDATION OR
     CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
     EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
     PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
     PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
     LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
     NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
     SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  '''
  
  import math
  from keras.models import Model
  from keras.layers import Input, GRU, CuDNNGRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Add, Bidirectional, MaxPooling1D, Activation
  from keras import backend as K
  from keras.initializers import Initializer
  from keras.callbacks import Callback
  from mdense import MDense
  import numpy as np
  import h5py
  import sys
  
  pcm_bits = 8
  embed_size = 128
  pcm_levels = 2**pcm_bits
  
  class Sparsify(Callback):
      def __init__(self, t_start, t_end, interval, density):
          super(Sparsify, self).__init__()
          self.batch = 0
          self.t_start = t_start
          self.t_end = t_end
          self.interval = interval
          self.final_density = density
  
      def on_batch_end(self, batch, logs=None):
          #print("batch number", self.batch)
          self.batch += 1
          if self.batch < self.t_start or ((self.batch-self.t_start) % self.interval != 0 and self.batch < self.t_end):
              #print("don't constrain");
              pass
          else:
              #print("constrain");
              layer = self.model.get_layer('gru_a')
              w = layer.get_weights()
              p = w[1]
              nb = p.shape[1]//p.shape[0]
              N = p.shape[0]
              #print("nb = ", nb, ", N = ", N);
              #print(p.shape)
              #print ("density = ", density)
              for k in range(nb):
                  density = self.final_density[k]
                  if self.batch < self.t_end:
                      r = 1 - (self.batch-self.t_start)/(self.t_end - self.t_start)
                      density = 1 - (1-self.final_density[k])*(1 - r*r*r)
                  A = p[:, k*N:(k+1)*N]
                  A = A - np.diag(np.diag(A))
                  A = np.transpose(A, (1, 0))
                  L=np.reshape(A, (N, N//16, 16))
                  S=np.sum(L*L, axis=-1)
                  SS=np.sort(np.reshape(S, (-1,)))
                  thresh = SS[round(N*N//16*(1-density))]
                  mask = (S>=thresh).astype('float32');
                  mask = np.repeat(mask, 16, axis=1)
                  mask = np.minimum(1, mask + np.diag(np.ones((N,))))
                  mask = np.transpose(mask, (1, 0))
                  p[:, k*N:(k+1)*N] = p[:, k*N:(k+1)*N]*mask
                  #print(thresh, np.mean(mask))
              w[1] = p
              layer.set_weights(w)
              
  
  class PCMInit(Initializer):
      def __init__(self, gain=.1, seed=None):
          self.gain = gain
          self.seed = seed
  
      def __call__(self, shape, dtype=None):
          num_rows = 1
          for dim in shape[:-1]:
              num_rows *= dim
          num_cols = shape[-1]
          flat_shape = (num_rows, num_cols)
          if self.seed is not None:
              np.random.seed(self.seed)
          a = np.random.uniform(-1.7321, 1.7321, flat_shape)
          #a[:,0] = math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows
          #a[:,1] = .5*a[:,0]*a[:,0]*a[:,0]
          a = a + np.reshape(math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows, (num_rows, 1))
          return self.gain * a
  
      def get_config(self):
          return {
              'gain': self.gain,
              'seed': self.seed
          }
  
  def new_lpcnet_model(frame_size = 160, rnn_units1=384, rnn_units2=16, nb_used_features = 38, training=False, use_gpu=True):
      pcm = Input(shape=(None, 3))
      feat = Input(shape=(None, nb_used_features))
      pitch = Input(shape=(None, 1))
      dec_feat = Input(shape=(None, 128))
      dec_state1 = Input(shape=(rnn_units1,))
      dec_state2 = Input(shape=(rnn_units2,))
  
      padding = 'valid' if training else 'same'
      fconv1 = Conv1D(128, 3, padding=padding, activation='tanh', name='feature_conv1')
      fconv2 = Conv1D(128, 3, padding=padding, activation='tanh', name='feature_conv2')
  
      embed = Embedding(256, embed_size, embeddings_initializer=PCMInit(), name='embed_sig')
      cpcm = Reshape((-1, embed_size*3))(embed(pcm))
  
      pembed = Embedding(256, 64, name='embed_pitch')
      cat_feat = Concatenate()([feat, Reshape((-1, 64))(pembed(pitch))])
      
      cfeat = fconv2(fconv1(cat_feat))
  
      fdense1 = Dense(128, activation='tanh', name='feature_dense1')
      fdense2 = Dense(128, activation='tanh', name='feature_dense2')
  
      cfeat = fdense2(fdense1(cfeat))
      
      rep = Lambda(lambda x: K.repeat_elements(x, frame_size, 1))
  
      if use_gpu:
          rnn = CuDNNGRU(rnn_units1, return_sequences=True, return_state=True, name='gru_a')
          rnn2 = CuDNNGRU(rnn_units2, return_sequences=True, return_state=True, name='gru_b')
      else:
          rnn = GRU(rnn_units1, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_a')
          rnn2 = GRU(rnn_units2, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_b')
  
      rnn_in = Concatenate()([cpcm, rep(cfeat)])
      md = MDense(pcm_levels, activation='softmax', name='dual_fc')
      gru_out1, _ = rnn(rnn_in)
      gru_out2, _ = rnn2(Concatenate()([gru_out1, rep(cfeat)]))
      ulaw_prob = md(gru_out2)
      
      model = Model([pcm, feat, pitch], ulaw_prob)
      model.rnn_units1 = rnn_units1
      model.rnn_units2 = rnn_units2
      model.nb_used_features = nb_used_features
      model.frame_size = frame_size
  
      encoder = Model([feat, pitch], cfeat)
      
      dec_rnn_in = Concatenate()([cpcm, dec_feat])
      dec_gru_out1, state1 = rnn(dec_rnn_in, initial_state=dec_state1)
      dec_gru_out2, state2 = rnn2(Concatenate()([dec_gru_out1, dec_feat]), initial_state=dec_state2)
      dec_ulaw_prob = md(dec_gru_out2)
  
      decoder = Model([pcm, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])
      return model, encoder, decoder
  