Avoid rounding the prediction multiple times

dnn/train_lpcnet.py

@@ -94,7 +94,7 @@ features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
 # Note: the LPC predictor output is now calculated by the loop below, this code was
 # for an ealier version that implemented the prediction filter in C
 
-upred = np.zeros((nb_frames*pcm_chunk_size,), dtype='int16')
+upred = np.zeros((nb_frames*pcm_chunk_size,), dtype='float32')
 
 # Use 16th order LPC to generate LPC prediction output upred[] and (in
 # mu-law form) pred[]
@@ -136,8 +136,8 @@ periods = (50*features[:,:,36:37]+100).astype('int16')
 in_data = np.concatenate([in_data, pred], axis=-1)
 
 # dump models to disk as we go
-checkpoint = ModelCheckpoint('lpcnet9c_384_10_G16_{epoch:02d}.h5')
+checkpoint = ModelCheckpoint('lpcnet14_384_10_G16_{epoch:02d}.h5')
 
-#model.load_weights('wavenet4f2_30.h5')
+#model.load_weights('lpcnet9b_384_10_G16_01.h5')
 model.compile(optimizer=Adam(0.001, amsgrad=True, decay=5e-5), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
 model.fit([in_data, in_exc, features, periods], out_data, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(2000, 40000, 400, 0.1)])