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wip...

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Jean-Marc Valin 2018-07-23 17:05:21 -04:00
parent 211435f5d3
commit b6af21f31c
2 changed files with 17 additions and 5 deletions
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7
dnn/gatedconv.py
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@ -1,6 +1,6 @@
from keras import backend as K
from keras.engine.topology import Layer
from keras.layers import activations, initializers, regularizers, constraints, InputSpec, Conv1D
from keras.layers import activations, initializers, regularizers, constraints, InputSpec, Conv1D, Dense
import numpy as np
class GatedConv(Conv1D):
@ -42,13 +42,16 @@ class GatedConv(Conv1D):
self.out_dims = filters
self.nongate_activation = activations.get(activation)
def call(self, inputs, memory=None):
def call(self, inputs, cond=None, memory=None):
if memory is None:
mem = K.zeros((K.shape(inputs)[0], self.mem_size, K.shape(inputs)[-1]))
else:
mem = K.variable(K.cast_to_floatx(memory))
inputs = K.concatenate([mem, inputs], axis=1)
ret = super(GatedConv, self).call(inputs)
if cond is not None:
d = Dense(2*self.out_dims, use_bias=False, activation='linear')
ret = ret + d(cond)
ret = self.nongate_activation(ret[:, :, :self.out_dims]) * activations.sigmoid(ret[:, :, self.out_dims:])
if self.return_memory:
ret = ret, inputs[:, :self.mem_size, :]

15
dnn/wavenet.py
View file

@ -4,6 +4,7 @@ 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 VarianceScaling
from mdense import MDense
import numpy as np
import h5py
@ -34,12 +35,20 @@ def new_wavenet_model(fftnet=False):
rfeat = rep(cfeat)
#tmp = Concatenate()([pcm, rfeat])
tmp = pcm
init = VarianceScaling(scale=1.5,mode='fan_avg',distribution='uniform')
for k in range(10):
res = tmp
tmp = Concatenate()([tmp, rfeat])
dilation = 9-k if fftnet else k
c = GatedConv(units, 2, dilation_rate=2**dilation, activation='tanh')
tmp = Dense(units, activation='relu')(c(tmp))
'''#tmp = Concatenate()([tmp, rfeat])
c = GatedConv(units, 2, dilation_rate=2**dilation, activation='tanh', kernel_initializer=init)
tmp = Dense(units, activation='relu')(c(tmp, cond=rfeat))'''
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])