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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 |
______________________________________________________________________________________________________________ |