ref: fa1d2824fad7fff313c335385de41d083df3c76f
dir: /dnn/wavenet.py/
#!/usr/bin/python3
import math
from keras.models import Model
from keras.layers import Input, LSTM, CuDNNGRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Add, Multiply, Bidirectional, MaxPooling1D, Activation
from keras import backend as K
from keras.initializers import Initializer
from keras.initializers import VarianceScaling
from mdense import MDense
import numpy as np
import h5py
import sys
from causalconv import CausalConv
from gatedconv import GatedConv
units=128
pcm_bits = 8
pcm_levels = 2**pcm_bits
nb_used_features = 38
class PCMInit(Initializer):
def __init__(self, gain=.1, seed=None):
self.gain = gain
self.seed = seed
def __call__(self, shape, dtype=None):
num_rows = 1
for dim in shape[:-1]:
num_rows *= dim
num_cols = shape[-1]
flat_shape = (num_rows, num_cols)
if self.seed is not None:
np.random.seed(self.seed)
a = np.random.uniform(-1.7321, 1.7321, flat_shape)
#a[:,0] = math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows
#a[:,1] = .5*a[:,0]*a[:,0]*a[:,0]
a = a + np.reshape(math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows, (num_rows, 1))
return self.gain * a
def get_config(self):
return {
'gain': self.gain,
'seed': self.seed
}
def new_wavenet_model(fftnet=False):
pcm = Input(shape=(None, 1))
pitch = Input(shape=(None, 1))
feat = Input(shape=(None, nb_used_features))
dec_feat = Input(shape=(None, 32))
fconv1 = Conv1D(128, 3, padding='same', activation='tanh')
fconv2 = Conv1D(32, 3, padding='same', activation='tanh')
cfeat = fconv2(fconv1(feat))
rep = Lambda(lambda x: K.repeat_elements(x, 160, 1))
activation='tanh'
rfeat = rep(cfeat)
#tmp = Concatenate()([pcm, rfeat])
embed = Embedding(256, units, embeddings_initializer=PCMInit())
tmp = Reshape((-1, units))(embed(pcm))
init = VarianceScaling(scale=1.5,mode='fan_avg',distribution='uniform')
for k in range(10):
res = tmp
dilation = 9-k if fftnet else k
tmp = Concatenate()([tmp, rfeat])
c = GatedConv(units, 2, dilation_rate=2**dilation, activation='tanh', kernel_initializer=init)
tmp = Dense(units, activation='relu')(c(tmp))
'''tmp = Concatenate()([tmp, rfeat])
c1 = CausalConv(units, 2, dilation_rate=2**dilation, activation='tanh')
c2 = CausalConv(units, 2, dilation_rate=2**dilation, activation='sigmoid')
tmp = Multiply()([c1(tmp), c2(tmp)])
tmp = Dense(units, activation='relu')(tmp)'''
if k != 0:
tmp = Add()([tmp, res])
md = MDense(pcm_levels, activation='softmax')
ulaw_prob = md(tmp)
model = Model([pcm, feat], ulaw_prob)
return model