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LPCNet
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deeper features

pull/8/head
Jean-Marc Valin 2018-10-03 22:30:44 -04:00
parent a6c55f2696
commit 5331ffed12
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GPG Key ID: 5E5DD9A36F9189C8
3 changed files with 13 additions and 7 deletions
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12
src/lpcnet.py
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@ -2,7 +2,7 @@
import math
from keras.models import Model
from keras.layers import Input, LSTM, CuDNNGRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Bidirectional, MaxPooling1D, Activation
from keras.layers import Input, LSTM, CuDNNGRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Add, Bidirectional, MaxPooling1D, Activation
from keras import backend as K
from keras.initializers import Initializer
from mdense import MDense
@ -47,14 +47,14 @@ def new_wavernn_model():
pitch = Input(shape=(None, 1))
feat = Input(shape=(None, nb_used_features))
pitch = Input(shape=(None, 1))
dec_feat = Input(shape=(None, 32))
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(32, 3, padding='same', activation='tanh')
fconv2 = Conv1D(102, 3, padding='same', activation='tanh')
if False:
cpcm = conv1(pcm)
@ -73,6 +73,12 @@ def new_wavernn_model():
cfeat = fconv2(fconv1(cat_feat))
fdense1 = Dense(128, activation='tanh')
fdense2 = Dense(128, activation='tanh')
cfeat = Add()([cfeat, cat_feat])
cfeat = fdense2(fdense1(cfeat))
rep = Lambda(lambda x: K.repeat_elements(x, 160, 1))
rnn = CuDNNGRU(rnn_units, return_sequences=True, return_state=True)

4
src/test_wavenet_audio.py
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@ -66,7 +66,7 @@ in_data = np.reshape(in_data, (nb_frames*pcm_chunk_size, 1))
out_data = np.reshape(data, (nb_frames*pcm_chunk_size, 1))
model.load_weights('wavenet4d2_203.h5')
model.load_weights('wavenet4f3_30.h5')
order = 16
@ -75,7 +75,7 @@ fexc = np.zeros((1, 1, 2), dtype='float32')
iexc = np.zeros((1, 1, 1), dtype='int16')
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])
cfeat = enc.predict([features[c:c+1, :, :nb_used_features], periods[c:c+1, :, :]])
for fr in range(1, feature_chunk_size):
f = c*feature_chunk_size + fr
a = features[c, fr, nb_features-order:]

4
src/train_wavenet_audio.py
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@ -104,8 +104,8 @@ in_data = np.concatenate([in_data, pred], axis=-1)
# f.create_dataset('data', data=in_data[:50000, :, :])
# f.create_dataset('feat', data=features[:50000, :, :])
checkpoint = ModelCheckpoint('wavenet4e_{epoch:02d}.h5')
checkpoint = ModelCheckpoint('wavenet4f3_{epoch:02d}.h5')
#model.load_weights('wavernn1c_01.h5')
#model.load_weights('wavenet4f2_30.h5')
model.compile(optimizer=Adam(0.001, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.fit([in_data, in_exc, features, periods], out_data, batch_size=batch_size, epochs=30, validation_split=0.2, callbacks=[checkpoint])