ref: 5d8a1313d621491a2bd6743740ef5385d9a6d89d
dir: /dnn/lpcnet.py/
#!/usr/bin/python3
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 import backend as K
from mdense import MDense
import numpy as np
import h5py
import sys
rnn_units=512
pcm_bits = 8
pcm_levels = 2**pcm_bits
nb_used_features = 37
def new_wavernn_model():
pcm = Input(shape=(None, 1))
pitch = Input(shape=(None, 1))
feat = Input(shape=(None, nb_used_features))
dec_feat = Input(shape=(None, 32))
dec_state = Input(shape=(rnn_units,))
conv1 = Conv1D(16, 7, padding='causal')
pconv1 = Conv1D(16, 5, padding='same')
pconv2 = Conv1D(16, 5, padding='same')
fconv1 = Conv1D(128, 3, padding='same')
fconv2 = Conv1D(32, 3, padding='same')
if False:
cpcm = conv1(pcm)
cpitch = pconv2(pconv1(pitch))
else:
cpcm = pcm
cpitch = pitch
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, cpitch, rep(cfeat)])
md = MDense(pcm_levels, activation='softmax')
gru_out, state = rnn(rnn_in)
ulaw_prob = md(gru_out)
model = Model([pcm, pitch, feat], ulaw_prob)
encoder = Model(feat, cfeat)
dec_rnn_in = Concatenate()([cpcm, cpitch, dec_feat])
dec_gru_out, state = rnn(dec_rnn_in, initial_state=dec_state)
dec_ulaw_prob = md(dec_gru_out)
decoder = Model([pcm, pitch, dec_feat, dec_state], [dec_ulaw_prob, state])
return model, encoder, decoder