shithub: opus

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deeper features

--- a/dnn/lpcnet.py

+++ b/dnn/lpcnet.py

@@ -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

@@ -47,7 +47,7 @@

     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')

@@ -54,7 +54,7 @@

     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 @@

     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)

--- a/dnn/test_wavenet_audio.py

+++ b/dnn/test_wavenet_audio.py

@@ -66,7 +66,7 @@

 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 @@

 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:]

--- a/dnn/train_wavenet_audio.py

+++ b/dnn/train_wavenet_audio.py

@@ -104,8 +104,8 @@

 # 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])

-- 

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