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sample_models.py
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sample_models.py
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from keras import backend as K
from keras.models import Model, Sequential
from keras.layers import (BatchNormalization, Conv1D, Conv2D, Dense, Input, Reshape, Lambda,
MaxPooling2D,GlobalAveragePooling1D,Dropout,RepeatVector, Flatten,
TimeDistributed, Activation, Bidirectional, SimpleRNN, GRU, LSTM)
def simple_rnn_model(input_dim, output_dim=29):
""" Build a recurrent network for speech
"""
# Main acoustic input
input_data = Input(name='the_input', shape=(None, input_dim))
# Add recurrent layer
simp_rnn = GRU(
output_dim,
return_sequences=True,
implementation=2,
name='rnn')(input_data)
# Add softmax activation layer
y_pred = Activation('softmax', name='softmax')(simp_rnn)
# Specify the model
model = Model(inputs=input_data, outputs=y_pred)
model.output_length = lambda x: x
print(model.summary())
return model
def rnn_model(input_dim, units, activation, output_dim=29, dropout_rate=0.5):
""" Build a recurrent network for speech
"""
# Main acoustic input
input_data = Input(name='the_input', shape=(None, input_dim))
# Add recurrent layer
simp_rnn = GRU(
units,
activation=activation,
return_sequences=True,
implementation=2,
name='rnn',
recurrent_dropout=dropout_rate,
dropout=dropout_rate)(input_data)
bn_rnn = BatchNormalization()(simp_rnn)
time_dense = TimeDistributed(Dense(output_dim))(bn_rnn)
# Add softmax activation layer
y_pred = Activation('softmax', name='softmax')(time_dense)
# Specify the model
model = Model(inputs=input_data, outputs=y_pred)
model.output_length = lambda x: x
print(model.summary())
return model
def cnn_rnn_model(input_dim, filters, kernel_size, conv_stride,
conv_border_mode, units, output_dim=29, dropout_rate=0.5):
""" Build a recurrent + convolutional network for speech
"""
# Main acoustic input
input_data = Input(name='the_input', shape=(None, input_dim))
# Add convolutional layer
conv_1d = Conv1D(filters, kernel_size,
strides=conv_stride,
padding=conv_border_mode,
activation='relu',
name='conv1d')(input_data)
# Add batch normalization
bn_cnn = BatchNormalization(name='bn_conv_1d')(conv_1d)
# Add a recurrent layer
simp_rnn = GRU(
units,
activation='relu',
return_sequences=True,
implementation=2,
name='rnn',
recurrent_dropout=dropout_rate,
dropout=dropout_rate)(bn_cnn)
# TODO: Add batch normalization
bn_rnn = BatchNormalization()(simp_rnn)
# TODO: Add a TimeDistributed(Dense(output_dim)) layer
time_dense = TimeDistributed(Dense(output_dim))(bn_rnn)
# Add softmax activation layer
y_pred = Activation('softmax', name='softmax')(time_dense)
# Specify the model
model = Model(inputs=input_data, outputs=y_pred)
model.output_length = lambda x: cnn_output_length(
x, kernel_size, conv_border_mode, conv_stride)
print(model.summary())
return model
def cnn_output_length(input_length, filter_size, border_mode, stride,
dilation=1):
""" Compute the length of the output sequence after 1D convolution along
time. Note that this function is in line with the function used in
Convolution1D class from Keras.
Params:
input_length (int): Length of the input sequence.
filter_size (int): Width of the convolution kernel.
border_mode (str): Only support `same` or `valid`.
stride (int): Stride size used in 1D convolution.
dilation (int)
"""
if input_length is None:
return None
assert border_mode in {'same', 'valid'}
dilated_filter_size = filter_size + (filter_size - 1) * (dilation - 1)
if border_mode == 'same':
output_length = input_length
elif border_mode == 'valid':
output_length = input_length - dilated_filter_size + 1
return (output_length + stride - 1) // stride
def deep_rnn_model(input_dim, units, recur_layers, output_dim=29, dropout_rate=0.5):
""" Build a deep recurrent network for speech
"""
# Main acoustic input
input_data = Input(name='the_input', shape=(None, input_dim))
bn_rnn = input_data
for _ in range(recur_layers):
bn_rnn = GRU(
units,
activation='relu',
return_sequences=True,
implementation=2,
recurrent_dropout=dropout_rate,
dropout=dropout_rate)(bn_rnn)
bn_rnn = BatchNormalization()(bn_rnn)
time_dense = TimeDistributed(Dense(output_dim))(bn_rnn)
# Add softmax activation layer
y_pred = Activation('softmax', name='softmax')(time_dense)
# Specify the model
model = Model(inputs=input_data, outputs=y_pred)
model.output_length = lambda x: x
print(model.summary())
return model
def bidirectional_rnn_model(input_dim, units, output_dim=29, dropout_rate=0.5):
""" Build a bidirectional recurrent network for speech
"""
# Main acoustic input
input_data = Input(name='the_input', shape=(None, input_dim))
bidir_rnn = Bidirectional(
GRU(
units,
activation='relu',
return_sequences=True,
implementation=2,
name='rnn',
recurrent_dropout=dropout_rate,
dropout=dropout_rate))(input_data)
time_dense = TimeDistributed(Dense(output_dim))(bidir_rnn)
# Add softmax activation layer
y_pred = Activation('softmax', name='softmax')(time_dense)
# Specify the model
model = Model(inputs=input_data, outputs=y_pred)
model.output_length = lambda x: x
print(model.summary())
return model
def cnn2d_rnn_model(input_dim, filters, kernel_size, conv_stride, conv_border_mode, pool_size, units, output_dim=29, dropout_rate=0.5):
""" Build a bidirectional recurrent network for speech
"""
# Main acoustic input
input_data = Input(name='the_input', shape=(None, input_dim))
nn = Lambda(lambda y: K.expand_dims(y, -1))(input_data)
# Add convolutional layer
nn = Conv2D(filters, kernel_size, padding=conv_border_mode, activation='relu')(nn)
nn = MaxPooling2D(pool_size)(nn)
nn = TimeDistributed(Flatten())(nn)
# Add a recurrent layer
nn = GRU(
units,
activation='relu',
return_sequences=True,
implementation=2,
recurrent_dropout=dropout_rate,
dropout=dropout_rate)(nn)
nn = TimeDistributed(Dense(output_dim))(nn)
# Add softmax activation layer
y_pred = Activation('softmax', name='softmax')(nn)
# Specify the model
model = Model(inputs=input_data, outputs=y_pred)
f1 = lambda x: cnn_output_length(x, 5, conv_border_mode, conv_stride)
f2 = lambda x: cnn_output_length(x, conv_stride, conv_border_mode, conv_stride)
model.output_length = lambda x: x
print(model.summary())
return model
def final_model(input_dim=161, filters=50, kernel_size=(11,11), conv_stride=1, conv_border_mode='same', pool_size=(1,5), units=200, recur_layers=2, output_dim=29, dropout_rate=0.50):
""" Build a deep network for speech
"""
# Main acoustic input
input_data = Input(name='the_input', shape=(None, input_dim))
nn = Lambda(lambda y: K.expand_dims(y, -1))(input_data)
# Add convolutional layer
nn = Conv2D(filters, kernel_size, padding=conv_border_mode, activation='relu')(nn)
nn = MaxPooling2D(pool_size)(nn)
nn = TimeDistributed(Flatten())(nn)
for _ in range(recur_layers):
nn = Bidirectional(
GRU(
units,
activation='relu',
return_sequences=True,
implementation=2,
recurrent_dropout=dropout_rate,
dropout=dropout_rate))(nn)
nn = BatchNormalization()(nn)
time_dense = TimeDistributed(Dense(output_dim))(nn)
# Add softmax activation layer
y_pred = Activation('softmax', name='softmax')(time_dense)
# Specify the model
model = Model(inputs=input_data, outputs=y_pred)
model.output_length = lambda x: x
print(model.summary())
return model