ref: 638252a965076cb6f63cd4b35f6676f8e2156974
parent: 824dbecaecaf0796696ee79081ebd70f1cffb455
author: Jean-Marc Valin <jmvalin@jmvalin.ca>
date: Tue Jul 10 09:56:59 EDT 2018
wip
--- a/dnn/test_lpcnet.py
+++ b/dnn/test_lpcnet.py
@@ -52,13 +52,23 @@
order = 16
pcm = 0.*out_data
+exc = 0.*out_data
+pitch = np.zeros((1, 1, 1), dtype='float32')
+iexc = np.zeros((1, 1, 1), dtype='float32')
+state = np.zeros((1, lpcnet.rnn_units), dtype='float32')
for c in range(1, nb_frames):
+ cfeat = enc.predict(features[c:c+1, :, :nb_used_features])
for fr in range(1, feature_chunk_size):
f = c*feature_chunk_size + fr
a = features[c, fr, nb_used_features+1:]
+
#print(a)
- gain = 1;
+ gain = 1.;
+ period = int(50*features[c, fr, 36]+100)
+ period = period - 4
for i in range(frame_size):
+ pitch[0, 0, 0] = exc[f*frame_size + i - period, 0]
+ #p, state = dec.predict([
pcm[f*frame_size + i, 0] = gain*out_data[f*frame_size + i, 0] - sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1, 0])
print(pcm[f*frame_size + i, 0])
--
⑨