Datasets:
HenryAI
/
KerasCodeExamples.txt

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# Expand the dimension to use 2D CNN.
x = layers.Reshape((-1, input_dim, 1), name=\"expand_dim\")(input_spectrogram)
# Convolution layer 1
x = layers.Conv2D(
filters=32,
kernel_size=[11, 41],
strides=[2, 2],
padding=\"same\",
use_bias=False,
name=\"conv_1\",
)(x)
x = layers.BatchNormalization(name=\"conv_1_bn\")(x)
x = layers.ReLU(name=\"conv_1_relu\")(x)
# Convolution layer 2
x = layers.Conv2D(
filters=32,
kernel_size=[11, 21],
strides=[1, 2],
padding=\"same\",
use_bias=False,
name=\"conv_2\",
)(x)
x = layers.BatchNormalization(name=\"conv_2_bn\")(x)
x = layers.ReLU(name=\"conv_2_relu\")(x)
# Reshape the resulted volume to feed the RNNs layers
x = layers.Reshape((-1, x.shape[-2] * x.shape[-1]))(x)
# RNN layers
for i in range(1, rnn_layers + 1):
recurrent = layers.GRU(
units=rnn_units,
activation=\"tanh\",
recurrent_activation=\"sigmoid\",
use_bias=True,
return_sequences=True,
reset_after=True,
name=f\"gru_{i}\",
)
x = layers.Bidirectional(
recurrent, name=f\"bidirectional_{i}\", merge_mode=\"concat\"
)(x)
if i < rnn_layers:
x = layers.Dropout(rate=0.5)(x)
# Dense layer
x = layers.Dense(units=rnn_units * 2, name=\"dense_1\")(x)
x = layers.ReLU(name=\"dense_1_relu\")(x)
x = layers.Dropout(rate=0.5)(x)
# Classification layer
output = layers.Dense(units=output_dim + 1, activation=\"softmax\")(x)
# Model
model = keras.Model(input_spectrogram, output, name=\"DeepSpeech_2\")
# Optimizer
opt = keras.optimizers.Adam(learning_rate=1e-4)
# Compile the model and return
model.compile(optimizer=opt, loss=CTCLoss)
return model
# Get the model
model = build_model(
input_dim=fft_length // 2 + 1,
output_dim=char_to_num.vocabulary_size(),
rnn_units=512,
)
model.summary(line_length=110)
Model: \"DeepSpeech_2\"
______________________________________________________________________________________________________________
Layer (type) Output Shape Param #
==============================================================================================================
input (InputLayer) [(None, None, 193)] 0
______________________________________________________________________________________________________________
expand_dim (Reshape) (None, None, 193, 1) 0
______________________________________________________________________________________________________________
conv_1 (Conv2D) (None, None, 97, 32) 14432
______________________________________________________________________________________________________________
conv_1_bn (BatchNormalization) (None, None, 97, 32) 128
______________________________________________________________________________________________________________
conv_1_relu (ReLU) (None, None, 97, 32) 0
______________________________________________________________________________________________________________
conv_2 (Conv2D) (None, None, 49, 32) 236544
______________________________________________________________________________________________________________
conv_2_bn (BatchNormalization) (None, None, 49, 32) 128
______________________________________________________________________________________________________________
conv_2_relu (ReLU) (None, None, 49, 32) 0
______________________________________________________________________________________________________________
reshape (Reshape) (None, None, 1568) 0
______________________________________________________________________________________________________________
bidirectional_1 (Bidirectional) (None, None, 1024) 6395904
______________________________________________________________________________________________________________
dropout (Dropout) (None, None, 1024) 0
______________________________________________________________________________________________________________
bidirectional_2 (Bidirectional) (None, None, 1024) 4724736
______________________________________________________________________________________________________________
dropout_1 (Dropout) (None, None, 1024) 0
______________________________________________________________________________________________________________
bidirectional_3 (Bidirectional) (None, None, 1024) 4724736
______________________________________________________________________________________________________________
dropout_2 (Dropout) (None, None, 1024) 0
______________________________________________________________________________________________________________
bidirectional_4 (Bidirectional) (None, None, 1024) 4724736
______________________________________________________________________________________________________________