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dropout_3 (Dropout) (None, None, 1024) 0 |
______________________________________________________________________________________________________________ |
bidirectional_5 (Bidirectional) (None, None, 1024) 4724736 |
______________________________________________________________________________________________________________ |
dense_1 (Dense) (None, None, 1024) 1049600 |
______________________________________________________________________________________________________________ |
dense_1_relu (ReLU) (None, None, 1024) 0 |
______________________________________________________________________________________________________________ |
dropout_4 (Dropout) (None, None, 1024) 0 |
______________________________________________________________________________________________________________ |
dense (Dense) (None, None, 32) 32800 |
============================================================================================================== |
Total params: 26,628,480 |
Trainable params: 26,628,352 |
Non-trainable params: 128 |
______________________________________________________________________________________________________________ |
Training and Evaluating |
# A utility function to decode the output of the network |
def decode_batch_predictions(pred): |
input_len = np.ones(pred.shape[0]) * pred.shape[1] |
# Use greedy search. For complex tasks, you can use beam search |
results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0] |
# Iterate over the results and get back the text |
output_text = [] |
for result in results: |
result = tf.strings.reduce_join(num_to_char(result)).numpy().decode(\"utf-8\") |
output_text.append(result) |
return output_text |
# A callback class to output a few transcriptions during training |
class CallbackEval(keras.callbacks.Callback): |
\"\"\"Displays a batch of outputs after every epoch.\"\"\" |
def __init__(self, dataset): |
super().__init__() |
self.dataset = dataset |
def on_epoch_end(self, epoch: int, logs=None): |
predictions = [] |
targets = [] |
for batch in self.dataset: |
X, y = batch |
batch_predictions = model.predict(X) |
batch_predictions = decode_batch_predictions(batch_predictions) |
predictions.extend(batch_predictions) |
for label in y: |
label = ( |
tf.strings.reduce_join(num_to_char(label)).numpy().decode(\"utf-8\") |
) |
targets.append(label) |
wer_score = wer(targets, predictions) |
print(\"-\" * 100) |
print(f\"Word Error Rate: {wer_score:.4f}\") |
print(\"-\" * 100) |
for i in np.random.randint(0, len(predictions), 2): |
print(f\"Target : {targets[i]}\") |
print(f\"Prediction: {predictions[i]}\") |
print(\"-\" * 100) |
Let's start the training process. |
# Define the number of epochs. |
epochs = 1 |
# Callback function to check transcription on the val set. |
validation_callback = CallbackEval(validation_dataset) |
# Train the model |
history = model.fit( |
train_dataset, |
validation_data=validation_dataset, |
epochs=epochs, |
callbacks=[validation_callback], |
) |
2021-09-28 21:16:48.067448: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8100 |
369/369 [==============================] - 586s 2s/step - loss: 300.4624 - val_loss: 296.1459 |
---------------------------------------------------------------------------------------------------- |
Word Error Rate: 0.9998 |
---------------------------------------------------------------------------------------------------- |
Target : the procession traversed ratcliffe twice halting for a quarter of an hour in front of the victims' dwelling |
Prediction: s |
---------------------------------------------------------------------------------------------------- |
Target : some difficulty then arose as to gaining admission to the strong room and it was arranged that a man may another custom house clerk |
Prediction: s |
---------------------------------------------------------------------------------------------------- |
Inference |
# Let's check results on more validation samples |
predictions = [] |
targets = [] |
for batch in validation_dataset: |
X, y = batch |
batch_predictions = model.predict(X) |
batch_predictions = decode_batch_predictions(batch_predictions) |
predictions.extend(batch_predictions) |
for label in y: |
label = tf.strings.reduce_join(num_to_char(label)).numpy().decode(\"utf-8\") |
targets.append(label) |
wer_score = wer(targets, predictions) |
print(\"-\" * 100) |
print(f\"Word Error Rate: {wer_score:.4f}\") |
print(\"-\" * 100) |