diff --git a/dnn/lpcnet.py b/dnn/lpcnet.py
index b07292dc..0ead18e5 100644
--- a/dnn/lpcnet.py
+++ b/dnn/lpcnet.py
@@ -10,7 +10,8 @@ import numpy as np
 import h5py
 import sys
 
-rnn_units=128
+rnn_units1=128
+rnn_units2=32
 pcm_bits = 8
 embed_size = 128
 pcm_levels = 2**pcm_bits
@@ -47,7 +48,8 @@ def new_wavernn_model():
     feat = Input(shape=(None, nb_used_features))
     pitch = Input(shape=(None, 1))
     dec_feat = Input(shape=(None, 128))
-    dec_state = Input(shape=(rnn_units,))
+    dec_state1 = Input(shape=(rnn_units1,))
+    dec_state2 = Input(shape=(rnn_units2,))
 
     fconv1 = Conv1D(128, 3, padding='same', activation='tanh')
     fconv2 = Conv1D(102, 3, padding='same', activation='tanh')
@@ -70,18 +72,21 @@ def new_wavernn_model():
     
     rep = Lambda(lambda x: K.repeat_elements(x, 160, 1))
 
-    rnn = CuDNNGRU(rnn_units, return_sequences=True, return_state=True)
+    rnn = CuDNNGRU(rnn_units1, return_sequences=True, return_state=True)
+    rnn2 = CuDNNGRU(rnn_units2, return_sequences=True, return_state=True)
     rnn_in = Concatenate()([cpcm, cexc, rep(cfeat)])
     md = MDense(pcm_levels, activation='softmax')
-    gru_out, state = rnn(rnn_in)
-    ulaw_prob = md(gru_out)
+    gru_out1, _ = rnn(rnn_in)
+    gru_out2, _ = rnn2(gru_out1)
+    ulaw_prob = md(gru_out2)
     
     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)
-    dec_ulaw_prob = md(dec_gru_out)
+    dec_gru_out1, state1 = rnn(dec_rnn_in, initial_state=dec_state1)
+    dec_gru_out2, state2 = rnn2(dec_gru_out1, initial_state=dec_state2)
+    dec_ulaw_prob = md(dec_gru_out2)
 
-    decoder = Model([pcm, exc, dec_feat, dec_state], [dec_ulaw_prob, state])
+    decoder = Model([pcm, exc, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])
     return model, encoder, decoder
diff --git a/dnn/test_wavenet_audio.py b/dnn/test_wavenet_audio.py
index 68d788cc..a8817d8a 100755
--- a/dnn/test_wavenet_audio.py
+++ b/dnn/test_wavenet_audio.py
@@ -59,14 +59,15 @@ in_data = np.reshape(in_data, (nb_frames*pcm_chunk_size, 1))
 out_data = np.reshape(data, (nb_frames*pcm_chunk_size, 1))
 
 
-model.load_weights('wavenet4f2_30.h5')
+model.load_weights('wavenet5d0_19.h5')
 
 order = 16
 
 pcm = 0.*out_data
 fexc = np.zeros((1, 1, 2), dtype='float32')
 iexc = np.zeros((1, 1, 1), dtype='int16')
-state = np.zeros((1, lpcnet.rnn_units), dtype='float32')
+state1 = np.zeros((1, lpcnet.rnn_units1), dtype='float32')
+state2 = np.zeros((1, lpcnet.rnn_units2), dtype='float32')
 for c in range(1, nb_frames):
     cfeat = enc.predict([features[c:c+1, :, :nb_used_features], periods[c:c+1, :, :]])
     for fr in range(1, feature_chunk_size):
@@ -82,7 +83,7 @@ for c in range(1, nb_frames):
             pred = -sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1, 0])
             fexc[0, 0, 1] = lin2ulaw(pred)
 
-            p, state = dec.predict([fexc, iexc, cfeat[:, fr:fr+1, :], state])
+            p, state1, state2 = dec.predict([fexc, iexc, cfeat[:, fr:fr+1, :], state1, state2])
             #p = p*p
             #p = p/(1e-18 + np.sum(p))
             p = np.maximum(p-0.001, 0).astype('float64')
diff --git a/dnn/train_wavenet_audio.py b/dnn/train_wavenet_audio.py
index 229f02de..3ad26db7 100755
--- a/dnn/train_wavenet_audio.py
+++ b/dnn/train_wavenet_audio.py
@@ -86,7 +86,7 @@ periods = (50*features[:,:,36:37]+100).astype('int16')
 
 in_data = np.concatenate([in_data, pred], axis=-1)
 
-checkpoint = ModelCheckpoint('wavenet5b_{epoch:02d}.h5')
+checkpoint = ModelCheckpoint('wavenet5d0_{epoch:02d}.h5')
 
 #model.load_weights('wavenet4f2_30.h5')
 model.compile(optimizer=Adam(0.001, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])