diff --git a/dnn/training_tf2/lpcnet.py b/dnn/training_tf2/lpcnet.py
index 8e83b42c..cbd73d83 100644
--- a/dnn/training_tf2/lpcnet.py
+++ b/dnn/training_tf2/lpcnet.py
@@ -313,7 +313,7 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features=20, batch_s
     
     if not flag_e2e:
         encoder = Model([feat, pitch], cfeat)
-        dec_rnn_in = Concatenate()([cpcm, dec_feat])
+        dec_rnn_in = Concatenate()([cpcm_decoder, dec_feat])
     else:
         encoder = Model([feat, pitch], [cfeat,lpcoeffs])
         dec_rnn_in = Concatenate()([cpcm_decoder, dec_feat])
@@ -324,5 +324,5 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features=20, batch_s
     if flag_e2e:
         decoder = Model([dpcm, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])
     else:
-        decoder = Model([pcm, dec_feat, dec_state1, dec_state2, lpcoeffs], [dec_ulaw_prob, state1, state2])
+        decoder = Model([dpcm, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])
     return model, encoder, decoder
diff --git a/dnn/training_tf2/test_lpcnet.py b/dnn/training_tf2/test_lpcnet.py
index 88439cf1..e1fdf59d 100755
--- a/dnn/training_tf2/test_lpcnet.py
+++ b/dnn/training_tf2/test_lpcnet.py
@@ -31,16 +31,22 @@ import numpy as np
 from ulaw import ulaw2lin, lin2ulaw
 import h5py
 
-# Flag for synthesizing e2e (differentiable lpc) model
-flag_e2e = False
+filename = sys.argv[1]
+with h5py.File(filename, "r") as f:
+    units = min(f['model_weights']['gru_a']['gru_a']['recurrent_kernel:0'].shape)
+    units2 = min(f['model_weights']['gru_b']['gru_b']['recurrent_kernel:0'].shape)
+    cond_size = min(f['model_weights']['feature_dense1']['feature_dense1']['kernel:0'].shape)
+    e2e = 'rc2lpc' in f['model_weights']
 
-model, enc, dec = lpcnet.new_lpcnet_model(training = False, flag_e2e = flag_e2e)
+
+model, enc, dec = lpcnet.new_lpcnet_model(training = False, rnn_units1=units, rnn_units2=units2, flag_e2e = e2e, cond_size=cond_size, batch_size=1)
 
 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
 #model.summary()
 
-feature_file = sys.argv[1]
-out_file = sys.argv[2]
+
+feature_file = sys.argv[2]
+out_file = sys.argv[3]
 frame_size = model.frame_size
 nb_features = 36
 nb_used_features = model.nb_used_features
@@ -56,7 +62,7 @@ periods = (.1 + 50*features[:,:,18:19]+100).astype('int16')
 
 
 
-model.load_weights('lpcnet38Sn_384_02.h5');
+model.load_weights(filename);
 
 order = 16
 
@@ -72,13 +78,13 @@ fout = open(out_file, 'wb')
 
 skip = order + 1
 for c in range(0, nb_frames):
-    if not flag_e2e:
+    if not e2e:
         cfeat = enc.predict([features[c:c+1, :, :nb_used_features], periods[c:c+1, :, :]])
     else:
         cfeat,lpcs = enc.predict([features[c:c+1, :, :nb_used_features], periods[c:c+1, :, :]])
     for fr in range(0, feature_chunk_size):
         f = c*feature_chunk_size + fr
-        if not flag_e2e:
+        if not e2e:
             a = features[c, fr, nb_features-order:]
         else:
             a = lpcs[c,fr]