def init(self, shape=(8, 2), flat_dim="time"):
self.batch_size = 1
# input data placeholder
input_tf = tf.keras.layers.Input(shape=shape,
batch_size=self.batch_size,
name="inp1")
# input test data
self.inputs = np.random.uniform(size=(self.batch_size, ) + shape)
# create non streamable trainable model
mode = Modes.TRAINING
if flat_dim == "time":
flat_tf = Stream(cell=tf.keras.layers.Flatten(),
mode=mode)(input_tf)
else:
flat_tf = tf.reshape(
input_tf,
(-1, input_tf.shape[1], input_tf.shape[2] * input_tf.shape[3]))
# flat_tf = flatten.Flatten(mode=mode, flat_dim=flat_dim)(input_tf)
self.model_train = tf.keras.Model(input_tf, flat_tf)
self.model_train.summary()
# output data, generated by non streaming model
self.outputs = self.model_train.predict(self.inputs)
return self.outputs
def model(flags):
"""Temporal Convolution ResNet model.
It is based on paper:
Temporal Convolution for Real-time Keyword Spotting on Mobile Devices
https://arxiv.org/pdf/1904.03814.pdf
Args:
flags: data/model parameters
Returns:
Keras model for training
"""
input_audio = tf.keras.layers.Input(shape=modes.get_input_data_shape(
flags, modes.Modes.TRAINING),
batch_size=flags.batch_size)
net = input_audio
if flags.preprocess == 'raw':
# it is a self contained model, user need to feed raw audio only
net = speech_features.SpeechFeatures(
speech_features.SpeechFeatures.get_params(flags))(net)
time_size, feature_size = net.shape[1:3]
channels = utils.parse(flags.channels)
net = tf.keras.backend.expand_dims(net)
if flags.debug_2d:
conv_kernel = first_conv_kernel = (3, 3)
else:
net = tf.reshape(
net, [-1, time_size, 1, feature_size]) # [batch, time, 1, feature]
first_conv_kernel = (3, 1)
conv_kernel = utils.parse(flags.kernel_size)
net = tf.keras.layers.Conv2D(filters=channels[0],
kernel_size=first_conv_kernel,
strides=1,
padding='same',
activation='linear')(net)
net = tf.keras.layers.BatchNormalization(momentum=flags.bn_momentum,
center=flags.bn_center,
scale=flags.bn_scale,
renorm=flags.bn_renorm)(net)
net = tf.keras.layers.Activation('relu')(net)
if utils.parse(flags.pool_size):
net = tf.keras.layers.AveragePooling2D(pool_size=utils.parse(
flags.pool_size),
strides=flags.pool_stride)(net)
channels = channels[1:]
# residual blocks
for n in channels:
if n != net.shape[-1]:
stride = 2
layer_in = tf.keras.layers.Conv2D(filters=n,
kernel_size=1,
strides=stride,
padding='same',
activation='linear')(net)
layer_in = tf.keras.layers.BatchNormalization(
momentum=flags.bn_momentum,
center=flags.bn_center,
scale=flags.bn_scale,
renorm=flags.bn_renorm)(layer_in)
layer_in = tf.keras.layers.Activation('relu')(layer_in)
else:
layer_in = net
stride = 1
net = tf.keras.layers.Conv2D(filters=n,
kernel_size=conv_kernel,
strides=stride,
padding='same',
activation='linear')(net)
net = tf.keras.layers.BatchNormalization(momentum=flags.bn_momentum,
center=flags.bn_center,
scale=flags.bn_scale,
renorm=flags.bn_renorm)(net)
net = tf.keras.layers.Activation('relu')(net)
net = tf.keras.layers.Conv2D(filters=n,
kernel_size=conv_kernel,
strides=1,
padding='same',
activation='linear')(net)
net = tf.keras.layers.BatchNormalization(momentum=flags.bn_momentum,
center=flags.bn_center,
scale=flags.bn_scale,
renorm=flags.bn_renorm)(net)
# residual connection
net = tf.keras.layers.Add()([net, layer_in])
net = tf.keras.layers.Activation('relu')(net)
net = tf.keras.layers.AveragePooling2D(pool_size=net.shape[1:3],
strides=1)(net)
net = tf.keras.layers.Dropout(rate=flags.dropout)(net)
# fully connected layer
net = tf.keras.layers.Conv2D(filters=flags.label_count,
kernel_size=1,
strides=1,
padding='same',
activation='linear')(net)
net = tf.reshape(net, shape=(-1, net.shape[3]))
if flags.return_softmax:
net = tf.keras.layers.Activation('softmax')(net)
return tf.keras.Model(input_audio, net)
def model(flags):
"""MatchboxNet model.
It is based on paper
MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network
Architecture for Speech Commands Recognition
https://arxiv.org/pdf/2004.08531.pdf
Args:
flags: data/model parameters
Returns:
Keras model for training
Raises:
ValueError: if any of input list has different length from any other;
or if padding is not supported
"""
ds_filters = parse(flags.ds_filters)
ds_repeat = parse(flags.ds_repeat)
ds_kernel_size = parse(flags.ds_kernel_size)
ds_stride = parse(flags.ds_stride)
ds_dilation = parse(flags.ds_dilation)
ds_residual = parse(flags.ds_residual)
for l in (ds_repeat, ds_kernel_size, ds_stride, ds_dilation, ds_residual):
if len(ds_filters) != len(l):
raise ValueError('all input lists have to be the same length')
input_audio = tf.keras.layers.Input(
shape=modes.get_input_data_shape(flags, modes.Modes.TRAINING),
batch_size=flags.batch_size)
net = input_audio
if flags.preprocess == 'raw':
# it is a self contained model, user need to feed raw audio only
net = speech_features.SpeechFeatures(
speech_features.SpeechFeatures.get_params(flags))(
net)
time_size, feature_size = net.shape[1:3]
net = tf.keras.backend.expand_dims(net)
net = tf.reshape(
net, [-1, time_size, 1, feature_size]) # [batch, time, 1, feature]
# encoder
for filters, repeat, kernel_size, stride, dilation, residual in zip(
ds_filters, ds_repeat, ds_kernel_size, ds_stride, ds_dilation,
ds_residual):
net = resnet_block(net, repeat, kernel_size, filters, dilation, stride,
residual, flags.padding, flags.dropout, flags.activation)
# decoder
net = stream.Stream(
cell=tf.keras.layers.AveragePooling2D(
pool_size=net.shape[1:3], strides=1))(
net)
net = tf.reshape(net, shape=(-1, net.shape[3]))
net = tf.keras.layers.Dense(units=flags.label_count)(net)
if flags.return_softmax:
net = tf.keras.layers.Activation('softmax')(net)
return tf.keras.Model(input_audio, net)