ref: 03fa20d5321f18ccbd133ffb4f8cc449fc8f4399
parent: a9835c4e5fbbdba5f9da356cf63a8ba881aacc27
author: Jean-Marc Valin <jmvalin@jmvalin.ca>
date: Tue Oct 9 08:27:02 EDT 2018
remove unused/dead code
--- a/dnn/lpcnet.py
+++ b/dnn/lpcnet.py
@@ -44,24 +44,13 @@
def new_wavernn_model():
pcm = Input(shape=(None, 2))
exc = Input(shape=(None, 1))
- pitch = Input(shape=(None, 1))
feat = Input(shape=(None, nb_used_features))
pitch = Input(shape=(None, 1))
dec_feat = Input(shape=(None, 128))
dec_state = Input(shape=(rnn_units,))
- conv1 = Conv1D(16, 7, padding='causal', activation='tanh')
- pconv1 = Conv1D(16, 5, padding='same', activation='tanh')
- pconv2 = Conv1D(16, 5, padding='same', activation='tanh')
fconv1 = Conv1D(128, 3, padding='same', activation='tanh')
fconv2 = Conv1D(102, 3, padding='same', activation='tanh')
-
- if False:
- cpcm = conv1(pcm)
- cpitch = pconv2(pconv1(pitch))
- else:
- cpcm = pcm
- cpitch = pitch
embed = Embedding(256, embed_size, embeddings_initializer=PCMInit())
cpcm = Reshape((-1, embed_size*2))(embed(pcm))
--- a/dnn/train_wavenet_audio.py
+++ b/dnn/train_wavenet_audio.py
@@ -58,14 +58,11 @@
pred_in = ulaw2lin(in_data)
for i in range(2, nb_frames*feature_chunk_size):
upred[i*frame_size:(i+1)*frame_size] = 0
- #if i % 100000 == 0:
- # print(i)
for k in range(16):
upred[i*frame_size:(i+1)*frame_size] = upred[i*frame_size:(i+1)*frame_size] - \
pred_in[i*frame_size-k:(i+1)*frame_size-k]*features[i, nb_features-16+k]
pred = lin2ulaw(upred)
-#pred = pred + np.random.randint(-1, 1, len(data))
in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))
@@ -88,12 +85,6 @@
periods = (50*features[:,:,36:37]+100).astype('int16')in_data = np.concatenate([in_data, pred], axis=-1)
-
-#in_data = np.concatenate([in_data, in_pitch], axis=-1)
-
-#with h5py.File('in_data.h5', 'w') as f:-# f.create_dataset('data', data=in_data[:50000, :, :])-# f.create_dataset('feat', data=features[:50000, :, :]) checkpoint = ModelCheckpoint('wavenet5b_{epoch:02d}.h5')--
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