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Use a single u-law embedding

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This commit is contained in:
Jean-Marc Valin 2019-01-21 16:52:57 -05:00
parent dc082d7c1c
commit b84a06dd08
4 changed files with 14 additions and 18 deletions
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15
dnn/lpcnet.py
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@ -114,8 +114,7 @@ class PCMInit(Initializer):
}
def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38, use_gpu=True):
pcm = Input(shape=(None, 2))
exc = Input(shape=(None, 1))
pcm = Input(shape=(None, 3))
feat = Input(shape=(None, nb_used_features))
pitch = Input(shape=(None, 1))
dec_feat = Input(shape=(None, 128))
@ -126,9 +125,7 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38, use_g
fconv2 = Conv1D(128, 3, padding='same', activation='tanh', name='feature_conv2')
embed = Embedding(256, embed_size, embeddings_initializer=PCMInit(), name='embed_sig')
cpcm = Reshape((-1, embed_size*2))(embed(pcm))
embed2 = Embedding(256, embed_size, embeddings_initializer=PCMInit(), name='embed_exc')
cexc = Reshape((-1, embed_size))(embed2(exc))
cpcm = Reshape((-1, embed_size*3))(embed(pcm))
pembed = Embedding(256, 64, name='embed_pitch')
cat_feat = Concatenate()([feat, Reshape((-1, 64))(pembed(pitch))])
@ -149,13 +146,13 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38, use_g
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, cexc, rep(cfeat)])
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)
model = Model([pcm, exc, feat, pitch], ulaw_prob)
model = Model([pcm, feat, pitch], ulaw_prob)
model.rnn_units1 = rnn_units1
model.rnn_units2 = rnn_units2
model.nb_used_features = nb_used_features
@ -163,10 +160,10 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38, use_g
encoder = Model([feat, pitch], cfeat)
dec_rnn_in = Concatenate()([cpcm, cexc, dec_feat])
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, exc, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])
decoder = Model([pcm, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])
return model, encoder, decoder