diff --git a/dnn/lpcnet.py b/dnn/lpcnet.py
index a275ce99..fcdf2425 100644
--- a/dnn/lpcnet.py
+++ b/dnn/lpcnet.py
@@ -46,6 +46,7 @@ def new_wavernn_model():
     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, 32))
     dec_state = Input(shape=(rnn_units,))
 
@@ -67,7 +68,10 @@ def new_wavernn_model():
     embed2 = Embedding(256, embed_size, embeddings_initializer=PCMInit())
     cexc = Reshape((-1, embed_size))(embed2(exc))
 
-    cfeat = fconv2(fconv1(feat))
+    pembed = Embedding(256, 64)
+    cat_feat = Concatenate()([feat, Reshape((-1, 64))(pembed(pitch))])
+    
+    cfeat = fconv2(fconv1(cat_feat))
 
     rep = Lambda(lambda x: K.repeat_elements(x, 160, 1))
 
@@ -77,8 +81,8 @@ def new_wavernn_model():
     gru_out, state = rnn(rnn_in)
     ulaw_prob = md(gru_out)
     
-    model = Model([pcm, exc, feat], ulaw_prob)
-    encoder = Model(feat, cfeat)
+    model = Model([pcm, exc, feat, pitch], ulaw_prob)
+    encoder = Model([feat, pitch], cfeat)
     
     dec_rnn_in = Concatenate()([cpcm, cexc, dec_feat])
     dec_gru_out, state = rnn(dec_rnn_in, initial_state=dec_state)
diff --git a/dnn/test_wavenet_audio.py b/dnn/test_wavenet_audio.py
index 01f63f91..3175ef3a 100755
--- a/dnn/test_wavenet_audio.py
+++ b/dnn/test_wavenet_audio.py
@@ -60,7 +60,7 @@ out_data = (out_data.astype('int16')+128).astype('uint8')
 features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
 features = features[:, :, :]
 
-
+periods = (50*features[:,:,36:37]+100).astype('int16')
 
 in_data = np.reshape(in_data, (nb_frames*pcm_chunk_size, 1))
 out_data = np.reshape(data, (nb_frames*pcm_chunk_size, 1))
diff --git a/dnn/train_wavenet_audio.py b/dnn/train_wavenet_audio.py
index 8856417b..9a304708 100755
--- a/dnn/train_wavenet_audio.py
+++ b/dnn/train_wavenet_audio.py
@@ -94,6 +94,7 @@ features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
 features = features[:, :, :nb_used_features]
 pred = np.reshape(pred, (nb_frames, pcm_chunk_size, 1))
 pred = (pred.astype('int16')+128).astype('uint8')
+periods = (50*features[:,:,36:37]+100).astype('int16')
 
 in_data = np.concatenate([in_data, pred], axis=-1)
 
@@ -103,8 +104,8 @@ in_data = np.concatenate([in_data, pred], axis=-1)
 # f.create_dataset('data', data=in_data[:50000, :, :])
 # f.create_dataset('feat', data=features[:50000, :, :])
 
-checkpoint = ModelCheckpoint('wavenet4d3_{epoch:02d}.h5')
+checkpoint = ModelCheckpoint('wavenet4e_{epoch:02d}.h5')
 
 #model.load_weights('wavernn1c_01.h5')
 model.compile(optimizer=Adam(0.001, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
-model.fit([in_data, in_exc, features], out_data, batch_size=batch_size, epochs=30, validation_split=0.2, callbacks=[checkpoint])
+model.fit([in_data, in_exc, features, periods], out_data, batch_size=batch_size, epochs=30, validation_split=0.2, callbacks=[checkpoint])