From 477d08734d8d716b8875df490787c6b8b59656f6 Mon Sep 17 00:00:00 2001
From: Jean-Marc Valin <jmvalin@jmvalin.ca>
Date: Fri, 23 Nov 2018 23:33:35 -0500
Subject: [PATCH] Dump embedding

---
 dnn/dump_lpcnet.py | 15 ++++++++++++---
 dnn/lpcnet.py      |  2 +-
 2 files changed, 13 insertions(+), 4 deletions(-)

diff --git a/dnn/dump_lpcnet.py b/dnn/dump_lpcnet.py
index f3556740..b1047460 100755
--- a/dnn/dump_lpcnet.py
+++ b/dnn/dump_lpcnet.py
@@ -42,7 +42,6 @@ def dump_gru_layer(self, f, hf):
     printVector(f, weights[0], name + '_weights')
     printVector(f, weights[1], name + '_recurrent_weights')
     printVector(f, weights[-1], name + '_bias')
-    #activation = re.search('function (.*) at', str(layer.activation)).group(1).upper()
     if hasattr(self, 'activation'):
         activation = self.activation.__name__.upper()
     else:
@@ -65,7 +64,6 @@ def dump_dense_layer(self, f, hf):
     weights = self.get_weights()
     printVector(f, weights[0], name + '_weights')
     printVector(f, weights[-1], name + '_bias')
-    #activation = re.search('function (.*) at', str(layer.activation)).group(1).upper()
     if hasattr(self, 'activation'):
         activation = self.activation.__name__.upper()
     else:
@@ -84,7 +82,6 @@ def dump_mdense_layer(self, f, hf):
     printVector(f, weights[0], name + '_weights')
     printVector(f, weights[1], name + '_bias')
     printVector(f, weights[1], name + '_factor')
-    #activation = re.search('function (.*) at', str(layer.activation)).group(1).upper()
     if hasattr(self, 'activation'):
         activation = self.activation.__name__.upper()
     else:
@@ -96,6 +93,18 @@ def dump_mdense_layer(self, f, hf):
     return False
 MDense.dump_layer = dump_mdense_layer
 
+def dump_embedding_layer(self, f, hf):
+    name = self.name
+    print("printing layer " + name + " of type " + self.__class__.__name__)
+    weights = self.get_weights()
+    printVector(f, weights[0], name + '_weights')
+    f.write('const EmbeddingLayer {} = {{\n   {}_weights,\n   {}, {}\n}};\n\n'
+            .format(name, name, weights[0].shape[0], weights[0].shape[1]))
+    hf.write('#define {}_SIZE {}\n'.format(name.upper(), weights[0].shape[1]))
+    hf.write('extern const EmbeddingLayer {};\n\n'.format(name));
+    return False
+Embedding.dump_layer = dump_embedding_layer
+
 
 model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=640, use_gpu=False)
 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
diff --git a/dnn/lpcnet.py b/dnn/lpcnet.py
index 54a714f0..3ab340c8 100644
--- a/dnn/lpcnet.py
+++ b/dnn/lpcnet.py
@@ -102,7 +102,7 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38, use_g
     embed2 = Embedding(256, embed_size, embeddings_initializer=PCMInit(), name='embed_exc')
     cexc = Reshape((-1, embed_size))(embed2(exc))
 
-    pembed = Embedding(256, 64)
+    pembed = Embedding(256, 64, name='embed_pitch')
     cat_feat = Concatenate()([feat, Reshape((-1, 64))(pembed(pitch))])
     
     cfeat = fconv2(fconv1(cat_feat))