def new_lpcnet_model(rnn_units1=384,
rnn_units2=16,
nb_used_features=38,
training=False,
use_gpu=True,
adaptation=False):
pcm = Input(shape=(None, 3))
feat = Input(shape=(None, nb_used_features))
pitch = Input(shape=(None, 1))
dec_feat = Input(shape=(None, 128))
dec_state1 = Input(shape=(rnn_units1, ))
dec_state2 = Input(shape=(rnn_units2, ))
padding = 'valid' if training else 'same'
fconv1 = Conv1D(128,
3,
padding=padding,
activation='tanh',
name='feature_conv1')
fconv2 = Conv1D(128,
3,
padding=padding,
activation='tanh',
name='feature_conv2')
embed = Embedding(256,
embed_size,
embeddings_initializer=PCMInit(),
name='embed_sig')
cpcm = Reshape((-1, embed_size * 3))(embed(pcm))
pembed = Embedding(256, 64, name='embed_pitch')
cat_feat = Concatenate()([feat, Reshape((-1, 64))(pembed(pitch))])
cfeat = fconv2(fconv1(cat_feat))
fdense1 = Dense(128, activation='tanh', name='feature_dense1')
fdense2 = Dense(128, activation='tanh', name='feature_dense2')
cfeat = fdense2(fdense1(cfeat))
rep = Lambda(lambda x: K.repeat_elements(x, frame_size, 1))
if use_gpu:
rnn = CuDNNGRU(rnn_units1,
return_sequences=True,
return_state=True,
name='gru_a')
rnn2 = CuDNNGRU(rnn_units2,
return_sequences=True,
return_state=True,
name='gru_b')
else:
rnn = GRU(rnn_units1,
return_sequences=True,
return_state=True,
recurrent_activation="sigmoid",
reset_after='true',
name='gru_a')
rnn2 = GRU(rnn_units2,
return_sequences=True,
return_state=True,
recurrent_activation="sigmoid",
reset_after='true',
name='gru_b')
rnn_in = Concatenate()([cpcm, rep(cfeat)])
md = MDense(pcm_levels, activation='softmax', name='dual_fc')
gru_out1, _ = rnn(rnn_in)
gru_out2, _ = rnn2(Concatenate()([gru_out1, rep(cfeat)]))
ulaw_prob = md(gru_out2)
if adaptation:
rnn.trainable = False
rnn2.trainable = False
md.trainable = False
embed.Trainable = False
model = Model([pcm, feat, pitch], ulaw_prob)
model.rnn_units1 = rnn_units1
model.rnn_units2 = rnn_units2
model.nb_used_features = nb_used_features
model.frame_size = frame_size
encoder = Model([feat, pitch], cfeat)
dec_rnn_in = Concatenate()([cpcm, dec_feat])
dec_gru_out1, state1 = rnn(dec_rnn_in, initial_state=dec_state1)
dec_gru_out2, state2 = rnn2(Concatenate()([dec_gru_out1, dec_feat]),
initial_state=dec_state2)
dec_ulaw_prob = md(dec_gru_out2)
decoder = Model([pcm, dec_feat, dec_state1, dec_state2],
[dec_ulaw_prob, state1, state2])
return model, encoder, decoder
