def padding(x1, x2):
if x1.shape[-2] > x2.shape[-2]:
adds = x1.shape[-2] - x2.shape[-2]
x2 = layers.ZeroPadding1D((0, adds))(x2)
elif x2.shape[-2] > x1.shape[-2]:
adds = x2.shape[-2] - x1.shape[-2]
x1 = layers.ZeroPadding1D((0, adds))(x1)
return x1, x2
def block2(x,
filters,
kernel_size=3,
stride=1,
conv_shortcut=False,
name=None):
"""A residual block.
Arguments:
x: input tensor.
filters: integer, filters of the bottleneck layer.
kernel_size: default 3, kernel size of the bottleneck layer.
stride: default 1, stride of the first layer.
conv_shortcut: default False, use convolution shortcut if True,
otherwise identity shortcut.
name: string, block label.
Returns:
Output tensor for the residual block.
"""
bn_axis = 2 if backend.image_data_format() == 'channels_last' else 1
preact = layers.BatchNormalization(axis=bn_axis,
epsilon=1.001e-5,
name=name + '_preact_bn')(x)
preact = layers.Activation('relu', name=name + '_preact_relu')(preact)
if conv_shortcut:
shortcut = layers.Conv1D(4 * filters,
1,
strides=stride,
name=name + '_0_conv')(preact)
else:
shortcut = layers.MaxPooling1D(1,
strides=stride)(x) if stride > 1 else x
x = layers.Conv1D(filters,
1,
strides=1,
use_bias=False,
name=name + '_1_conv')(preact)
x = layers.BatchNormalization(axis=bn_axis,
epsilon=1.001e-5,
name=name + '_1_bn')(x)
x = layers.Activation('relu', name=name + '_1_relu')(x)
x = layers.ZeroPadding1D(padding=((1, ), (1, )), name=name + '_2_pad')(x)
x = layers.Conv1D(filters,
kernel_size,
strides=stride,
use_bias=False,
name=name + '_2_conv')(x)
x = layers.BatchNormalization(axis=bn_axis,
epsilon=1.001e-5,
name=name + '_2_bn')(x)
x = layers.Activation('relu', name=name + '_2_relu')(x)
x = layers.Conv1D(4 * filters, 1, name=name + '_3_conv')(x)
x = layers.Add(name=name + '_out')([shortcut, x])
return x
def create_model(input_shape, params):
batch_size = params['batch_size']
lahead = params['lahead']
# input_shape = input_shape.shape[-2:]
X_input = Input(input_shape)
X = X_input
X = layers.ZeroPadding1D(padding=2)(X)
# X = layers.TimeDistributed(Dense(input_shape[-1],
# kernel_constraint=constraints.max_norm(1.0),
# activation='tanh'))(X)
X = Conv1D(filters=32,
kernel_size=6,
strides=1,
name='conv1',
kernel_initializer=glorot_uniform(seed=0))(X)
X = layers.GaussianNoise(.005)(X)
# X = layers.AveragePooling1D(2, strides=1)(X)
X = conv_block(X, f=3, filters=[48, 48, 96], stage=2, block='a', strides=2)
X = layers.GaussianNoise(.005)(X)
X = identity_block(X,
f=3,
filters=[48, 48, 96],
stage=2,
block='b',
strides=1)
X = layers.GaussianNoise(.005)(X)
X = identity_block(X,
f=3,
filters=[48, 48, 96],
stage=2,
block='c',
strides=1)
print(X.get_shape())
print(X_input.get_shape())
# X = layers.concatenate([X, X_input])
# X = layers.AveragePooling1D(2, name="avg_pool")(X)
# rnn_cells = [tf.keras.layers.LSTMCell(128) for _ in range(2)]
# stacked_lstm = tf.keras.layers.StackedRNNCells(rnn_cells)
# lstm_layer = tf.keras.layers.RNN(stacked_lstm)
# X = lstm_layer(X)
X = layers.Flatten()(X)
X = Dense(256)(X)
X = Dense(lahead, kernel_initializer=glorot_uniform(seed=0))(X)
model = Model(inputs=X_input, outputs=X, name='model1')
model.compile(loss='mse',
optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001))
return model
def __call__(self, x):
shortcut = x
prefix = 'expanded_conv/'
infilters = x.shape[-1]
if self.block_id:
# Expand
prefix = 'expanded_conv_{}/'.format(self.block_id)
x = layers.Conv1D(_depth(infilters * self.expansion),
kernel_size=1,
padding='same',
use_bias=False,
name=prefix + 'expand')(x)
x = layers.BatchNormalization(epsilon=1e-3,
momentum=0.999,
name=prefix + 'expand/BatchNorm')(x)
x = self.activation(x)
if self.stride == 2:
x = layers.ZeroPadding1D(padding=1,
name=prefix + 'depthwise/pad')(x)
x = layers.SeparableConv1D(
int(x.shape[-1]),
self.kernel_size,
strides=self.stride,
padding='same' if self.stride == 1 else 'valid',
use_bias=False,
name=prefix + 'depthwise')(x)
x = layers.BatchNormalization(epsilon=1e-3,
momentum=0.999,
name=prefix + "depthwise/BatchNorm")(x)
x = self.activation(x)
if self.se_ratio:
x = SEBlock(_depth(infilters * self.expansion), self.se_ratio,
prefix)(x)
x = layers.Conv1D(self.filters,
kernel_size=1,
padding='same',
use_bias=False,
name=prefix + 'project')(x)
x = layers.BatchNormalization(epsilon=1e-3,
momentum=0.999,
name=prefix + "project/BatchNorm")(x)
if self.stride == 1 and infilters == self.filters:
x = layers.Add(name=prefix + 'Add')([shortcut, x])
return x
def getSiameseModel():
left_input = layers.Input((waveform_len, 2))
right_input = layers.Input((waveform_len, 2))
dummy_input = layers.Input((waveform_len, 2))
l = layers.BatchNormalization()(dummy_input)
l = layers.ZeroPadding1D(padding=2)(l)
# General inception-style layers
l = layers.Conv1D(64, 2, activation='relu')(l)
l = layers.MaxPooling1D()(l)
l = layers.Conv1D(64, 4, activation='relu')(l)
l = layers.MaxPooling1D()(l)
l = layers.Conv1D(32, 8, activation='relu')(l)
l = layers.MaxPooling1D()(l)
l = layers.Conv1D(32, 16, activation='relu')(l)
l = layers.MaxPooling1D()(l)
l = layers.Conv1D(32, 32, activation='relu')(l)
l = layers.MaxPooling1D()(l)
l = layers.Flatten()(l)
l = layers.Dense(256, activation="sigmoid")(l)
extractor = models.Model(inputs=[dummy_input], outputs=[l])
#intermediate model with fingerprints on both sides
encoded_l = models.Model(
inputs=[left_input],
outputs=[extractor.call(left_input)
]) #call() cuts off old input layer and attaches new one
encoded_r = models.Model(inputs=[right_input],
outputs=[extractor.call(right_input)])
# Add a customized layer to compute the absolute difference between the encodings
L1_layer = layers.Lambda(
lambda tensors: tf.keras.backend.abs(tensors[0] - tensors[1]))
L1_distance = L1_layer([encoded_l.output, encoded_r.output])
# Add a dense layer with a sigmoid unit to generate the similarity score
prediction = layers.Dense(1, activation='sigmoid')(L1_distance)
# Connect the inputs with the outputs
siamese_net = models.Model(inputs=[left_input, right_input],
outputs=prediction)
# return the model
return siamese_net
def get_simplegru_model(num_units=64, num_layers=2, dropout=0.1):
input = Input((5, 2), name='dust_input')
inputs_expanded = layers.ZeroPadding1D((0, 1))(input)
#region=Input((),name='region_input',dtype=tf.uint8)
#month=Input((),name='month_input',dtype=tf.float32)
#cells=[layers.GRUCell(num_units) for _ in range(num_layers-1)]
#cells.append(layers.GRUCell(num_units,dropout=dropout))
#multi_gru=layers.RNN(cells,name='multi-lstm')
multi_gru = layers.LSTM(num_units, activation='relu')
# batch X num_units
output = multi_gru(inputs_expanded)
# batch X 6 X num_units
repeated = layers.RepeatVector(6)(output)
# batch X 6 X num_units
lstm_seq = layers.LSTM(num_units, return_sequences=True,
activation='relu')(repeated)
#flattened=layers.Flatten()(inputs)
# (batch,num_units)
#feature=layers.Dense(num_units,activations.relu)(output)
#region_one=layers.Lambda(region_one_hot,name='region_one_hot')(region)
#month_one=layers.Lambda(month_one_hot,name='month_one_hot')(month)
# (batch,num_units+10+12)
#feature_concat=layers.Concatenate()([feature,month_one])
#feature_concat=layers.Dropout(0.1)(feature_concat)
#dense_final=layers.Dense(16,activations.relu)(feature)
#tiny=layers.Dense(1,name='tiny_dense')(dense_final)
#micro=layers.Dense(1,name='micro_dense')(dense_final)
#model=Model(inputs=[inputs,region,month],outputs=[tiny,micro])
final = layers.TimeDistributed(layers.Dense(2))(lstm_seq)
model = Model(inputs=input, outputs=final)
return model
def res_tcn_block(x,
n_filters,
kernel_size,
dilation,
dropout_rate=0.2,
l2=keras.regularizers.l2(0.),
use_batch_norm=True):
global name_
prev_x = x
for _ in reversed(range(2)):
x = layers.ZeroPadding1D(((kernel_size - 1) * dilation, 0))(x)
x = layers.Conv1D(
n_filters,
kernel_size=kernel_size,
dilation_rate=dilation,
kernel_regularizer=l2,
bias_regularizer=l2,
kernel_initializer=keras.initializers.Orthogonal())(x)
if use_batch_norm:
x = layers.BatchNormalization()(x)
x = layers.Activation(tf.nn.relu)(x)
x = layers.SpatialDropout1D(rate=dropout_rate)(inputs=x)
# 1x1 conv to match the shapes (channel dimension).
if prev_x.shape[-1] != n_filters:
prev_x = layers.Conv1D(n_filters,
1,
padding='same',
activation='relu',
name='match_channel' + str(name_))(prev_x)
name_ += 1
res_x = keras.layers.add([x, prev_x])
res_x = layers.Activation(tf.nn.relu)(res_x)
return res_x # , x
def get_layers(context = 300, filt_length_conv_1 = 150, filt_length_conv_2 = 5,
number_filters_conv_1 = 100, number_filters_conv_2 = 80, fc_cells = 512):
'''
Returns the paramters that for a CNN based model.
Consider chaning default parameters as per application.
Architecture: CNN->FC->Logits
Reference:
"Analysis of CNN-based Speech Recognition System using Raw Speech as Input"
(https://ronan.collobert.com/pub/matos/2015_cnnspeech_interspeech.pdf)
'''
y_true = tf.keras.layers.Input((None,), name="y_true")
y_true_length = tf.keras.layers.Input((1), name="y_true_length")
# Inputshape [btch_sz, n_time, n_channels = 1]
input_audio = layers.Input(shape=(None, 1), name="audio_input")
# Append zeros in time for context at the beggining and end of audio sample
input_audio_padded = layers.ZeroPadding1D(padding=(context), name = 'zero_padding_context')(input_audio)
# Apply 1d filter: Shape [btch_sz, n_time, n_channels]
filt_length_conv_1 = 2 * context + 1 + filt_length_conv_1
conv = layers.Conv1D(number_filters_conv_1, filt_length_conv_1, strides=100, activation='relu', name="conv_1")(input_audio_padded)
conv = layers.Conv1D(number_filters_conv_2, filt_length_conv_2, strides = 1, activation='relu', name="conv_2")(conv)
conv = layers.Conv1D(number_filters_conv_2, filt_length_conv_2, strides = 1, activation='relu', name="conv_3")(conv)
# Apply FC layer to each time step
fc = layers.TimeDistributed(layers.Dense(fc_cells), name = 'fc')(conv)
fc = layers.ReLU()(fc)
fc = layers.Dropout(rate=0.1)(fc)
# Apply FC layer to each time step to output prob distro across chars
logits = layers.TimeDistributed(layers.Dense(29, activation='softmax'), name = 'logits')(fc)
return logits, input_audio, y_true, y_true_length
def __call__(self, ip):
with backend.name_scope('separable_conv_block_%s' % self.block_id):
x = layers.Activation('relu')(ip)
if self.strides == 2:
x = layers.ZeroPadding1D(padding=self.kernel_size,
name='separable_conv_1_pad_%s' %
self.block_id)(x)
conv_pad = 'valid'
else:
conv_pad = 'same'
x = layers.SeparableConv1D(self.filters,
self.kernel_size,
strides=self.strides,
name='separable_conv_1_%s' %
self.block_id,
padding=conv_pad,
use_bias=False,
kernel_initializer='he_normal')(x)
x = layers.BatchNormalization(momentum=0.9997,
epsilon=1e-3,
name='separable_conv_1_bn_%s' %
self.block_id)(x)
x = layers.Activation('relu')(x)
x = layers.SeparableConv1D(self.filters,
self.kernel_size,
name='separable_conv_2_%s' %
self.block_id,
padding='same',
use_bias=False,
kernel_initializer='he_normal')(x)
x = layers.BatchNormalization(momentum=0.9997,
epsilon=1e-3,
name='separable_conv_2_bn_%s' %
self.block_id)(x)
return x
def block3(x,
filters,
kernel_size=3,
stride=1,
groups=32,
conv_shortcut=True,
name=None):
"""A residual block.
Arguments:
x: input tensor.
filters: integer, filters of the bottleneck layer.
kernel_size: default 3, kernel size of the bottleneck layer.
stride: default 1, stride of the first layer.
groups: default 32, group size for grouped convolution.
conv_shortcut: default True, use convolution shortcut if True,
otherwise identity shortcut.
name: string, block label.
Returns:
Output tensor for the residual block.
"""
bn_axis = 2 if backend.image_data_format() == 'channels_last' else 1
if conv_shortcut:
shortcut = layers.Conv1D((64 // groups) * filters,
1,
strides=stride,
use_bias=False,
name=name + '_0_conv')(x)
shortcut = layers.BatchNormalization(axis=bn_axis,
epsilon=1.001e-5,
name=name + '_0_bn')(shortcut)
else:
shortcut = x
x = layers.Conv1D(filters, 1, use_bias=False, name=name + '_1_conv')(x)
x = layers.BatchNormalization(axis=bn_axis,
epsilon=1.001e-5,
name=name + '_1_bn')(x)
x = layers.Activation('relu', name=name + '_1_relu')(x)
c = filters // groups
x = layers.ZeroPadding1D(padding=((1, ), (1, )), name=name + '_2_pad')(x)
x = layers.DepthwiseConv2D(kernel_size,
strides=stride,
depth_multiplier=c,
use_bias=False,
name=name + '_2_conv')(x) # TODO
x_shape = backend.int_shape(x)[1:-1]
x = layers.Reshape(x_shape + (groups, c, c))(x)
x = layers.Lambda(lambda x: sum(x[:, :, :, :, i] for i in range(c)),
name=name + '_2_reduce')(x)
x = layers.Reshape(x_shape + (filters, ))(x)
x = layers.BatchNormalization(axis=bn_axis,
epsilon=1.001e-5,
name=name + '_2_bn')(x)
x = layers.Activation('relu', name=name + '_2_relu')(x)
x = layers.Conv1D((64 // groups) * filters,
1,
use_bias=False,
name=name + '_3_conv')(x)
x = layers.BatchNormalization(axis=bn_axis,
epsilon=1.001e-5,
name=name + '_3_bn')(x)
x = layers.Add(name=name + '_add')([shortcut, x])
x = layers.Activation('relu', name=name + '_out')(x)
return x
def DenseNet(number,
include_top=True,
weights='hasc',
input_shape=None,
pooling=None,
classes=6,
classifier_activation='softmax'):
if input_shape is None:
input_shape = (256 * 3, 1)
if weights in ['hasc', 'HASC'] and include_top and classes != 6:
raise ValueError('If using `weights` as `"hasc"` with `include_top`'
' as true, `classes` should be 6')
# number of blocks
if number == 121:
blocks = [6, 12, 24, 16]
elif number == 169:
blocks = [6, 12, 32, 32]
elif number == 201:
blocks = [6, 12, 48, 32]
else:
raise ValueError('`number` should be 121, 169 or 201')
inputs = layers.Input(shape=input_shape)
x = layers.ZeroPadding1D(padding=(3, 3))(inputs)
x = layers.Conv1D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
x = layers.BatchNormalization(epsilon=1.001e-5, name='conv1/bn')(x)
x = layers.Activation('relu', name='conv1/relu')(x)
x = layers.ZeroPadding1D(padding=(1, 1))(x)
x = layers.MaxPooling1D(3, strides=2, name='pool1')(x)
x = DenseBlock(blocks[0], name='conv2')(x)
x = TransitionBlock(0.5, name='pool2')(x)
x = DenseBlock(blocks[1], name='conv3')(x)
x = TransitionBlock(0.5, name='pool3')(x)
x = DenseBlock(blocks[2], name='conv4')(x)
x = TransitionBlock(0.5, name='pool4')(x)
x = DenseBlock(blocks[3], name='conv5')(x)
x = layers.BatchNormalization(epsilon=1.001e-5, name='bn')(x)
x = layers.Activation('relu', name='relu')(x)
x = layers.GlobalAveragePooling1D(name='avg_pool')(x)
y = layers.Dense(classes,
activation=classifier_activation,
name='predictions')(x)
# Create model.
model_ = Model(inputs, y)
if weights is not None:
if weights in ['hasc', "HASC"]:
weights = 'weights/densenet{}/densenet{}_hasc_weights_{}_{}.hdf5'.format(
number, number, int(input_shape[0]), int(input_shape[1]))
# hasc or weights fileで初期化
if os.path.exists(weights):
print("Load weights from {}".format(weights))
model_.load_weights(weights)
else:
print("Not exist weights: {}".format(weights))
# topを含まないとき
if not include_top:
if pooling is None:
# topを削除する
model_ = Model(inputs=model_.input,
outputs=model_.layers[-3].output)
elif pooling == 'avg':
y = layers.GlobalAveragePooling1D()(model_.layers[-3].output)
model_ = Model(inputs=model_.input, outputs=y)
elif pooling == 'max':
y = layers.GlobalMaxPooling1D()(model_.layers[-3].output)
model_ = Model(inputs=model_.input, outputs=y)
else:
print("Not exist pooling option: {}".format(pooling))
model_ = Model(inputs=model_.input,
outputs=model_.layers[-3].output)
return model_
def __call__(self, ip, p):
with backend.name_scope('normal_A_block_%s' % self.block_id):
p = AdjustBlock(self.filters, self.block_id)(p, ip)
h = layers.Activation('relu')(ip)
h = layers.Conv1D(self.filters,
1,
strides=1,
padding='same',
name='normal_conv_1_%s' % self.block_id,
use_bias=False,
kernel_initializer='he_normal')(h)
h = layers.BatchNormalization(momentum=0.9997,
epsilon=1e-3,
name='normal_bn_1_%s' %
self.block_id)(h)
with backend.name_scope('block_1'):
x1_1 = SeparableConvBlock(self.filters,
kernel_size=5,
block_id='normal_left1_%s' %
self.block_id)(h)
x1_2 = SeparableConvBlock(self.filters,
block_id='normal_right1_%s' %
self.block_id)(p)
x1_1, x1_2 = padding(x1_1, x1_2)
x1 = layers.add([x1_1, x1_2],
name='normal_add_1_%s' % self.block_id)
with backend.name_scope('block_2'):
x2_1 = SeparableConvBlock(self.filters,
kernel_size=5,
block_id='normal_left2_%s' %
self.block_id)(p)
x2_2 = SeparableConvBlock(self.filters,
kernel_size=3,
block_id='normal_right2_%s' %
self.block_id)(p)
x2 = layers.add([x2_1, x2_2],
name='normal_add_2_%s' % self.block_id)
with backend.name_scope('block_3'):
x3 = layers.AveragePooling1D(3,
strides=1,
padding='same',
name='normal_left3_%s' %
self.block_id)(h)
x3, p = padding(x3, p)
x3 = layers.add([x3, p],
name='normal_add_3_%s' % self.block_id)
with backend.name_scope('block_4'):
x4_1 = layers.AveragePooling1D(3,
strides=1,
padding='same',
name='normal_left4_%s' %
self.block_id)(p)
x4_2 = layers.AveragePooling1D(3,
strides=1,
padding='same',
name='normal_right4_%s' %
self.block_id)(p)
x4 = layers.add([x4_1, x4_2],
name='normal_add_4_%s' % self.block_id)
with backend.name_scope('block_5'):
x5 = SeparableConvBlock(self.filters,
block_id='normal_left5_%s' %
self.block_id)(h)
x5 = layers.add([x5, h],
name='normal_add_5_%s' % self.block_id)
if x2.shape[-2] < p.shape[-2]:
adds = p.shape[-2] - x2.shape[-2]
x2 = layers.ZeroPadding1D((0, adds))(x2)
x = layers.concatenate([p, x1, x2, x3, x4, x5],
name='normal_concat_%s' % self.block_id)
return x, ip
def f(x):
y = layers.ZeroPadding1D(padding=1,
name="padding{}{}_branch2a".format(
stage_char, block_char))(x)
y = layers.Conv1D(filters,
kernel_size,
strides=stride,
use_bias=False,
name="res{}{}_branch2a".format(
stage_char, block_char),
**parameters)(y)
y = BatchNormalizationFreeze(axis=axis,
epsilon=1e-5,
freeze=freeze_bn,
name="bn{}{}_branch2a".format(
stage_char, block_char))(y)
y = layers.Activation("relu",
name="res{}{}_branch2a_relu".format(
stage_char, block_char))(y)
y = layers.ZeroPadding1D(padding=1,
name="padding{}{}_branch2b".format(
stage_char, block_char))(y)
y = layers.Conv1D(filters,
kernel_size,
use_bias=False,
name="res{}{}_branch2b".format(
stage_char, block_char),
**parameters)(y)
y = BatchNormalizationFreeze(axis=axis,
epsilon=1e-5,
freeze=freeze_bn,
name="bn{}{}_branch2b".format(
stage_char, block_char))(y)
if block == 0:
shortcut = layers.Conv1D(filters,
1,
strides=stride,
use_bias=False,
name="res{}{}_branch1".format(
stage_char, block_char),
**parameters)(x)
shortcut = BatchNormalizationFreeze(axis=axis,
epsilon=1e-5,
freeze=freeze_bn,
name="bn{}{}_branch1".format(
stage_char,
block_char))(shortcut)
else:
shortcut = x
y = layers.Add(name="res{}{}".format(stage_char, block_char))(
[y, shortcut])
y = layers.Activation("relu",
name="res{}{}_relu".format(
stage_char, block_char))(y)
return y
def get_layers(context = 300, filt_length_conv_1 = 150, filt_length_conv_2 = 5,
number_filters_conv_1 = 40, number_filters_conv_2 = 256, LSTM_cells = 832, fc_cells = 512):
'''
Returns the paramters that for a CNN based model.
Consider changing default parameters as per application.
Architecture: CNN->CNN->LSTM->LSTM->LSTM->FC->Logits
Reference:
"Learning the Speech Front-end With Raw Waveform CLDNNs"
(https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43960.pdf)
'''
y_true = tf.keras.layers.Input((None,), name="y_true")
y_true_length = tf.keras.layers.Input((1), name="y_true_length")
# Inputshape [btch_sz, n_time, n_channels = 1]
input_audio = layers.Input(shape=(None, 1), name="audio_input")
# Append zeros in time for context at the beggining and end of audio sample
input_audio_padded = layers.ZeroPadding1D(padding=(context), name = 'zero_padding_context')(input_audio)
# Apply 1d filter: Shape [btch_sz, n_time, n_channels]
filt_length_conv_1 = 2 * context + 1 + filt_length_conv_1
conv = layers.Conv1D(number_filters_conv_1,
filt_length_conv_1,
kernel_initializer = tf.glorot_normal_initializer(),
strides=100,
activation='relu',
name="conv_1")(input_audio_padded)
conv = layers.Conv1D(number_filters_conv_2,
filt_length_conv_2,
kernel_initializer = tf.glorot_normal_initializer(),
strides = 1,
activation='relu',
name="conv_2")(conv)
# 3 Layers of LSTMS
rnn = tf.keras.layers.LSTM(LSTM_cells,
kernel_initializer = initializers.RandomUniform(minval=-0.02, maxval=0.02),
return_sequences = True,
name = 'rnn_1')(conv)
rnn = tf.keras.layers.LSTM(LSTM_cells,
kernel_initializer = initializers.RandomUniform(minval=-0.02, maxval=0.02),
return_sequences = True,
name = 'rnn_2')(rnn)
rnn = tf.keras.layers.LSTM(LSTM_cells,
kernel_initializer = initializers.RandomUniform(minval=-0.02, maxval=0.02),
return_sequences = True,
name = 'rnn_3')(rnn)
# Apply FC layer to each time step
fc = layers.TimeDistributed(layers.Dense(fc_cells,
kernel_initializer = tf.glorot_normal_initializer(),
), name = 'fc')(rnn)
fc = layers.ReLU()(fc)
fc = layers.Dropout(rate=0.1)(fc)
# Apply FC layer to each time step to output prob distro across chars
logits = layers.TimeDistributed(layers.Dense(29,
kernel_initializer = tf.glorot_normal_initializer(),
activation='softmax'), name = 'logits')(fc)
return logits, input_audio, y_true, y_true_length
def call(self, inputs, training=None, mask=None):
return layers.Concatenate(self.axis)(
[inputs[0],
layers.ZeroPadding1D(
(int(self.diff / 2 if self.diff % 2 == 0 else self.diff / 2 + 1), int(self.diff / 2))
)(inputs[1])])
def __init__(self, kernel_size, num_filters):
self.conv = layers.Conv1D(num_filters, kernel_size, activation='relu')
self.conv_gate = layers.Conv1D(num_filters,
kernel_size,
activation="sigmoid")
self.pad_input = layers.ZeroPadding1D(padding=(kernel_size - 1, 0))
def __call__(self, p, ip):
ip_shape = ip.shape
if p is not None:
p_shape = p.shape
with backend.name_scope('adjust_block'):
if p is None:
p = ip
elif p_shape[-2] != ip_shape[-2]:
with backend.name_scope('adjust_reduction_block_%s' %
self.block_id):
p = layers.Activation('relu',
name='adjust_relu_1_%s' %
self.block_id)(p)
p1 = layers.AveragePooling1D(1,
strides=2,
padding='valid',
name='adjust_avg_pool_1_%s' %
self.block_id)(p)
p1 = layers.Conv1D(self.filters // 2,
1,
padding='same',
use_bias=False,
name='adjust_conv_1_%s' % self.block_id,
kernel_initializer='he_normal')(p1)
p2 = layers.ZeroPadding1D((0, 1))(p)
p2 = layers.Cropping1D((1, 0))(p2)
p2 = layers.AveragePooling1D(1,
strides=2,
padding='valid',
name='adjust_avg_pool_2_%s' %
self.block_id)(p2)
p2 = layers.Conv1D(self.filters // 2,
1,
padding='same',
use_bias=False,
name='adjust_conv_2_%s' % self.block_id,
kernel_initializer='he_normal')(p2)
p = layers.concatenate([p1, p2], axis=-1)
p = layers.BatchNormalization(momentum=0.9997,
epsilon=1e-3,
name='adjust_bn_%s' %
self.block_id)(p)
elif p_shape[-1] != self.filters:
with backend.name_scope('adjust_projection_block_%s' %
self.block_id):
p = layers.Activation('relu')(p)
p = layers.Conv1D(self.filters,
1,
strides=1,
padding='same',
name='adjust_conv_projection_%s' %
self.block_id,
use_bias=False,
kernel_initializer='he_normal')(p)
p = layers.BatchNormalization(momentum=0.9997,
epsilon=1e-3,
name='adjust_bn_%s' %
self.block_id)(p)
return p
def __call__(self, ip, p):
with backend.name_scope('reduction_A_block_%s' % self.block_id):
p = AdjustBlock(self.filters, self.block_id)(p, ip)
h = layers.Activation('relu')(ip)
h = layers.Conv1D(self.filters,
1,
strides=1,
padding='same',
name='reduction_conv_1_%s' % self.block_id)(h)
h = layers.BatchNormalization(momentum=0.9997,
epsilon=1e-3,
name='reduction_bn_1_%s' %
self.block_id)(h)
h3 = layers.ZeroPadding1D(3,
name='reduction_pad_1_%s' %
self.block_id)(h)
with backend.name_scope('block_1'):
x1_1 = SeparableConvBlock(self.filters,
5,
strides=2,
block_id='reduction_left1_%s' %
self.block_id)(h)
x1_2 = SeparableConvBlock(self.filters,
7,
strides=2,
block_id='reduction_right1_%s' %
self.block_id)(p)
x1_1, x1_2 = padding(x1_1, x1_2)
x1 = layers.add([x1_1, x1_2],
name='reduction_add_1_%s' % self.block_id)
with backend.name_scope('block_2'):
x2_1 = layers.MaxPooling1D(3,
strides=2,
padding='valid',
name='reduction_left2_%s' %
self.block_id)(h3)
x2_2 = SeparableConvBlock(self.filters,
7,
strides=2,
block_id='reduction_right2_%s' %
self.block_id)(p)
x2_1, x2_2 = padding(x2_1, x2_2)
x2 = layers.add([x2_1, x2_2],
name='reduction_add_2_%s' % self.block_id)
with backend.name_scope('block_3'):
x3_1 = layers.AveragePooling1D(3,
strides=2,
padding='valid',
name='reduction_left3_%s' %
self.block_id)(h3)
x3_2 = SeparableConvBlock(self.filters,
5,
strides=2,
block_id='reduction_right3_%s' %
self.block_id)(p)
x3_1, x3_2 = padding(x3_1, x3_2)
x3 = layers.add([x3_1, x3_2],
name='reduction_add3_%s' % self.block_id)
with backend.name_scope('block_4'):
x4 = layers.AveragePooling1D(3,
strides=1,
padding='same',
name='reduction_left4_%s' %
self.block_id)(x1)
x2, x4 = padding(x2, x4)
x4 = layers.add([x2, x4])
with backend.name_scope('block_5'):
x5_1 = SeparableConvBlock(self.filters,
3,
block_id='reduction_left4_%s' %
self.block_id)(x1)
x5_2 = layers.MaxPooling1D(3,
strides=2,
padding='valid',
name='reduction_right5_%s' %
self.block_id)(h3)
x5_1, x5_2 = padding(x5_1, x5_2)
x5 = layers.add([x5_1, x5_2],
name='reduction_add4_%s' % self.block_id)
x3 = layers.ZeroPadding1D((0, 1))(x3)
x = layers.concatenate([x2, x3, x4, x5],
name='reduction_concat_%s' % self.block_id)
return x, ip