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deeper features

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This commit is contained in:
Jean-Marc Valin 2018-10-03 22:30:44 -04:00
parent 639766b322
commit ea1391e174
3 changed files with 13 additions and 7 deletions
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12
dnn/lpcnet.py
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@ -2,7 +2,7 @@
import math import math
from keras.models import Model 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 import backend as K
from keras.initializers import Initializer from keras.initializers import Initializer
from mdense import MDense from mdense import MDense
@ -47,14 +47,14 @@ def new_wavernn_model():
pitch = Input(shape=(None, 1)) pitch = Input(shape=(None, 1))
feat = Input(shape=(None, nb_used_features)) feat = Input(shape=(None, nb_used_features))
pitch = Input(shape=(None, 1)) pitch = Input(shape=(None, 1))
dec_feat = Input(shape=(None, 32)) dec_feat = Input(shape=(None, 128))
dec_state = Input(shape=(rnn_units,)) dec_state = Input(shape=(rnn_units,))
conv1 = Conv1D(16, 7, padding='causal', activation='tanh') conv1 = Conv1D(16, 7, padding='causal', activation='tanh')
pconv1 = Conv1D(16, 5, padding='same', activation='tanh') pconv1 = Conv1D(16, 5, padding='same', activation='tanh')
pconv2 = Conv1D(16, 5, padding='same', activation='tanh') pconv2 = Conv1D(16, 5, padding='same', activation='tanh')
fconv1 = Conv1D(128, 3, 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: if False:
cpcm = conv1(pcm) cpcm = conv1(pcm)
@ -73,6 +73,12 @@ def new_wavernn_model():
cfeat = fconv2(fconv1(cat_feat)) 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)) rep = Lambda(lambda x: K.repeat_elements(x, 160, 1))
rnn = CuDNNGRU(rnn_units, return_sequences=True, return_state=True) rnn = CuDNNGRU(rnn_units, return_sequences=True, return_state=True)

4
dnn/test_wavenet_audio.py
View file

@ -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)) 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 order = 16
@ -75,7 +75,7 @@ fexc = np.zeros((1, 1, 2), dtype='float32')
iexc = np.zeros((1, 1, 1), dtype='int16') iexc = np.zeros((1, 1, 1), dtype='int16')
state = np.zeros((1, lpcnet.rnn_units), dtype='float32') state = np.zeros((1, lpcnet.rnn_units), dtype='float32')
for c in range(1, nb_frames): 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): for fr in range(1, feature_chunk_size):
f = c*feature_chunk_size + fr f = c*feature_chunk_size + fr
a = features[c, fr, nb_features-order:] a = features[c, fr, nb_features-order:]

4
dnn/train_wavenet_audio.py
View file

@ -104,8 +104,8 @@ in_data = np.concatenate([in_data, pred], axis=-1)
# f.create_dataset('data', data=in_data[:50000, :, :]) # f.create_dataset('data', data=in_data[:50000, :, :])
# f.create_dataset('feat', data=features[: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.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]) model.fit([in_data, in_exc, features, periods], out_data, batch_size=batch_size, epochs=30, validation_split=0.2, callbacks=[checkpoint])