diff --git a/src/train_wavenet_audio.py b/src/train_wavenet_audio.py
index 3ad26db..2c5998e 100755
--- a/src/train_wavenet_audio.py
+++ b/src/train_wavenet_audio.py
@@ -1,4 +1,8 @@
 #!/usr/bin/python3
+# train_wavenet_audio.py
+# Jean-Marc Valin
+#
+# Train a CELPNet model (note not a Wavenet model)
 
 import wavenet
 import lpcnet
@@ -13,38 +17,55 @@ import h5py
 import tensorflow as tf
 from keras.backend.tensorflow_backend import set_session
 config = tf.ConfigProto()
+
+# use this option to reserve GPU memory, e.g. for running more than
+# one thing at a time.  Best to disable for GPUs with small memory
 config.gpu_options.per_process_gpu_memory_fraction = 0.44
+
 set_session(tf.Session(config=config))
 
 nb_epochs = 40
+
+# Try reducing batch_size if you run out of memory on your GPU
 batch_size = 64
 
+# Note we are creating a CELPNet model
+
 #model = wavenet.new_wavenet_model(fftnet=True)
 model, _, _ = lpcnet.new_wavernn_model()
 
 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
 model.summary()
 
-exc_file = sys.argv[1]
+exc_file = sys.argv[1]     # not used at present
 feature_file = sys.argv[2]
-pred_file = sys.argv[3]
-pcm_file = sys.argv[4]
+pred_file = sys.argv[3]    # LPC predictor samples. Not used at present, see below
+pcm_file = sys.argv[4]     # 16 bit unsigned short PCM samples
 frame_size = 160
 nb_features = 55
 nb_used_features = lpcnet.nb_used_features
 feature_chunk_size = 15
 pcm_chunk_size = frame_size*feature_chunk_size
 
+# u for unquantised, load 16 bit PCM samples and convert to mu-law
+
 udata = np.fromfile(pcm_file, dtype='int16')
 data = lin2ulaw(udata)
 nb_frames = len(data)//pcm_chunk_size
 
 features = np.fromfile(feature_file, dtype='float32')
 
+# limit to discrete number of frames
 data = data[:nb_frames*pcm_chunk_size]
 udata = udata[:nb_frames*pcm_chunk_size]
 features = features[:nb_frames*feature_chunk_size*nb_features]
 
+# Noise injection: the idea is that the real system is going to be
+# predicting samples based on previously predicted samples rather than
+# from the original. Since the previously predicted samples aren't
+# expected to be so good, I add noise to the training data.  Exactly
+# how the noise is added makes a huge difference
+
 in_data = np.concatenate([data[0:1], data[:-1]]);
 noise = np.concatenate([np.zeros((len(data)*1//5)), np.random.randint(-3, 3, len(data)*1//5), np.random.randint(-2, 2, len(data)*1//5), np.random.randint(-1, 1, len(data)*2//5)])
 in_data = in_data + noise
@@ -52,9 +73,15 @@ in_data = np.clip(in_data, 0, 255)
 
 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.fromfile(pred_file, dtype='int16')
 upred = upred[:nb_frames*pcm_chunk_size]
 
+# Use 16th order LPC to generate LPC prediction output upred[] and (in
+# mu-law form) pred[]
+
 pred_in = ulaw2lin(in_data)
 for i in range(2, nb_frames*feature_chunk_size):
     upred[i*frame_size:(i+1)*frame_size] = 0
@@ -64,9 +91,13 @@ for i in range(2, nb_frames*feature_chunk_size):
 
 pred = lin2ulaw(upred)
 
-
 in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))
 in_data = in_data.astype('uint8')
+
+# LPC residual, which is the difference between the input speech and
+# the predictor output, with a slight time shift this is also the
+# ideal excitation in_exc
+
 out_data = lin2ulaw(udata-upred)
 in_exc = np.concatenate([out_data[0:1], out_data[:-1]]);
 
@@ -86,6 +117,7 @@ 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('wavenet5d0_{epoch:02d}.h5')
 
 #model.load_weights('wavenet4f2_30.h5')