diff --git a/dnn/lpcnet.py b/dnn/lpcnet.py
index d0fb01c5..b07292dc 100644
--- a/dnn/lpcnet.py
+++ b/dnn/lpcnet.py
@@ -44,25 +44,14 @@ class PCMInit(Initializer):
 def new_wavernn_model():
     pcm = Input(shape=(None, 2))
     exc = Input(shape=(None, 1))
-    pitch = Input(shape=(None, 1))
     feat = Input(shape=(None, nb_used_features))
     pitch = Input(shape=(None, 1))
     dec_feat = Input(shape=(None, 128))
     dec_state = Input(shape=(rnn_units,))
 
-    conv1 = Conv1D(16, 7, padding='causal', activation='tanh')
-    pconv1 = Conv1D(16, 5, padding='same', activation='tanh')
-    pconv2 = Conv1D(16, 5, padding='same', activation='tanh')
     fconv1 = Conv1D(128, 3, padding='same', activation='tanh')
     fconv2 = Conv1D(102, 3, padding='same', activation='tanh')
 
-    if False:
-        cpcm = conv1(pcm)
-        cpitch = pconv2(pconv1(pitch))
-    else:
-        cpcm = pcm
-        cpitch = pitch
-
     embed = Embedding(256, embed_size, embeddings_initializer=PCMInit())
     cpcm = Reshape((-1, embed_size*2))(embed(pcm))
     embed2 = Embedding(256, embed_size, embeddings_initializer=PCMInit())
diff --git a/dnn/train_wavenet_audio.py b/dnn/train_wavenet_audio.py
index 5acc6c4d..229f02de 100755
--- a/dnn/train_wavenet_audio.py
+++ b/dnn/train_wavenet_audio.py
@@ -58,14 +58,11 @@ upred = upred[:nb_frames*pcm_chunk_size]
 pred_in = ulaw2lin(in_data)
 for i in range(2, nb_frames*feature_chunk_size):
     upred[i*frame_size:(i+1)*frame_size] = 0
-    #if i % 100000 == 0:
-    #    print(i)
     for k in range(16):
         upred[i*frame_size:(i+1)*frame_size] = upred[i*frame_size:(i+1)*frame_size] - \
             pred_in[i*frame_size-k:(i+1)*frame_size-k]*features[i, nb_features-16+k]
 
 pred = lin2ulaw(upred)
-#pred = pred + np.random.randint(-1, 1, len(data))
 
 
 in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))
@@ -89,12 +86,6 @@ periods = (50*features[:,:,36:37]+100).astype('int16')
 
 in_data = np.concatenate([in_data, pred], axis=-1)
 
-#in_data = np.concatenate([in_data, in_pitch], axis=-1)
-
-#with h5py.File('in_data.h5', 'w') as f:
-# f.create_dataset('data', data=in_data[:50000, :, :])
-# f.create_dataset('feat', data=features[:50000, :, :])
-
 checkpoint = ModelCheckpoint('wavenet5b_{epoch:02d}.h5')
 
 #model.load_weights('wavenet4f2_30.h5')