44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
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import tensorflow as tf
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from tensorflow import keras
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from jiwer import wer
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import numpy as np
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from preprocessing import *
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def decode_batch_predictions(pred):
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input_len = np.ones(pred.shape[0]) * pred.shape[1]
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results = keras.backend.ctc_decode(pred,
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input_length = input_len,
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greedy = True)[0][0]
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output_text = []
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for result in results:
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result = tf.strings.reduce_join(num_to_char(result)).numpy().decode("utf-8")
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output_text.append(result)
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return output_text
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class ce(keras.callbacks.Callback):
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def __init__(self, dataset, model):
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super().__init__()
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self.dataset = dataset
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self.____model = model
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def on_epoch_end(self, epoch, logs = None):
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predictions = []
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targets = []
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for batch in self.dataset:
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X, y = batch
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batch_predictions = self.____model.predict(X, verbose = 0)
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batch_predictions = decode_batch_predictions(batch_predictions)
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predictions.extend(batch_predictions)
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for label in y:
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label = (
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tf.strings.reduce_join(num_to_char(label)).numpy().decode("utf-8")
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)
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targets.append(label)
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wer_score = wer(targets, predictions)
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print(f"Word Error Rate: {wer_score:.4f}")
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for i in np.random.randint(0, len(predictions), 10):
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print(f"Target : {targets[i]}")
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print(f"Prediction: {predictions[i]}")
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