CODEWITHSUNDEEP
pythontensorflowspeech-recognitionmlp
A. Gehani
A. Gehani

Reputation: 93

MLP for speech recognition

I am trying to learn speech recognition and so I am using a simple MLP for starters.

Below is the code:

#Simple MLP model

num_labels = Y.shape[1]
filter_size = 2

# Construct model 
model = Sequential()

model.add(Dense(256, input_shape=(32,)))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(num_labels))
model.add(Activation('softmax'))

# Compile the model
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')

# Display model architecture summary 
model.summary()

# Calculate pre-training accuracy 

score = model.evaluate(X_test, Y_test, verbose=2)
accuracy = 100*score[1]

print("Pre-training accuracy: %.4f%%" % accuracy)

I am using MFCC for feature extraction and MLB for one-hot encoding.

The shape of X_train,X_val,X_test,Y_train,Y_val and Y_test is as follows: (54296, 99, 32) (6787, 99, 32) (6788, 99, 32) (54296, 31) (6787, 31) (6788, 31)

I am getting following errors:

  1. WARNING:tensorflow:Model was constructed with shape (None, 32) for input Tensor("dense_21_input:0", shape=(None, 32), dtype=float32), but it was called on an input with incompatible shape (None, 99, 32).

When I change the input_shape to (99,32,) the warning disappears. Can anybody explain me the reason?

  1. ValueError: Shapes (None, 31) and (None, 99, 31) are incompatible (This one is when I try to calculate the pre-training accuracy)

I have no idea on how to deal with this error?

I look forward to receiving some help.

Thanks!

Upvotes: 0

Views: 99

Answers (1)

Ayush
Ayush

Reputation: 1630

In this particular line, model.add(Dense(256, input_shape=(32,))), you define the input shape to be (32, ) which means the shape is going to be of the form (batch_size, 32) which isn't really the case, because your inputs are of the shape (batch_size, 99, 32) so that's the reason you need the specification like so: input_shape = (99, 32, )

About the pre-training accuracy part, I'm not really sure because you mention the shape of X_test to be (6788, 99, 32), the last dimension being 32, while your Y_test has the shape (6788, 31), the last dimension being 31.
That doesn't quite add up, the error in your code says Shapes (None, 31) and (None, 99, 31), the (None, 99, 31)part is inconsistent with the shape mentioned earlier.

Upvotes: 0

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