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Use excitation as input

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This commit is contained in:
Jean-Marc Valin 2018-08-24 00:20:10 -04:00
parent 8f6e490ba2
commit c381db5688
3 changed files with 29 additions and 17 deletions
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17
dnn/lpcnet.py
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@ -12,6 +12,7 @@ import sys
rnn_units=512
pcm_bits = 8
embed_size = 128
pcm_levels = 2**pcm_bits
nb_used_features = 38
@ -42,6 +43,7 @@ class PCMInit(Initializer):
def new_wavernn_model():
pcm = Input(shape=(None, 2))
exc = Input(shape=(None, 1))
pitch = Input(shape=(None, 1))
feat = Input(shape=(None, nb_used_features))
dec_feat = Input(shape=(None, 32))
@ -60,26 +62,27 @@ def new_wavernn_model():
cpcm = pcm
cpitch = pitch
embed = Embedding(256, 128, embeddings_initializer=PCMInit())
cpcm = Reshape((-1, 128*2))(embed(pcm))
embed = Embedding(256, embed_size, embeddings_initializer=PCMInit())
cpcm = Reshape((-1, embed_size*2))(embed(pcm))
embed2 = Embedding(256, embed_size, embeddings_initializer=PCMInit())
cexc = Reshape((-1, embed_size))(embed2(exc))
cfeat = fconv2(fconv1(feat))
rep = Lambda(lambda x: K.repeat_elements(x, 160, 1))
rnn = CuDNNGRU(rnn_units, return_sequences=True, return_state=True)
rnn_in = Concatenate()([cpcm, rep(cfeat)])
rnn_in = Concatenate()([cpcm, cexc, rep(cfeat)])
md = MDense(pcm_levels, activation='softmax')
gru_out, state = rnn(rnn_in)
ulaw_prob = md(gru_out)
model = Model([pcm, feat], ulaw_prob)
model = Model([pcm, exc, feat], ulaw_prob)
encoder = Model(feat, cfeat)
dec_rnn_in = Concatenate()([cpcm, dec_feat])
dec_rnn_in = Concatenate()([cpcm, cexc, dec_feat])
dec_gru_out, state = rnn(dec_rnn_in, initial_state=dec_state)
dec_ulaw_prob = md(dec_gru_out)
decoder = Model([pcm, dec_feat, dec_state], [dec_ulaw_prob, state])
decoder = Model([pcm, exc, dec_feat, dec_state], [dec_ulaw_prob, state])
return model, encoder, decoder