2017-10-25 11:22:45 +00:00
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from speech_siamese import siamese_model
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2017-10-25 10:08:03 +00:00
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from record_mic_speech import record_spectrogram
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2017-10-25 11:22:45 +00:00
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from importlib import reload
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2017-11-03 08:49:19 +00:00
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# import speech_data
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# reload(speech_data)
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from speech_data import create_test_pair,get_word_pairs_data,speech_data
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2017-10-25 11:22:45 +00:00
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import numpy as np
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2017-10-25 10:08:03 +00:00
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2017-10-25 11:22:45 +00:00
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model = siamese_model((15, 1654))
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model.load_weights('./models/siamese_speech_model-final.h5')
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2017-10-26 07:18:31 +00:00
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def predict_recording_with(m,sample_size=15):
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spec1 = record_spectrogram(n_sec=1.4)
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spec2 = record_spectrogram(n_sec=1.4)
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inp = create_test_pair(spec1,spec2,sample_size)
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return m.predict([inp[:, 0], inp[:, 1]])
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2017-11-03 08:49:19 +00:00
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# while(True):
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# print(predict_recording_with(model))
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2017-10-26 07:18:31 +00:00
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2017-11-03 08:49:19 +00:00
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def test_with(audio_group):
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X,Y = speech_data(audio_group)
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print(np.argmax(model.predict([X[:, 0], X[:, 1]]),axis=1))
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print(Y.astype(np.int8))
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test_with('rand_edu')
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2017-11-02 07:44:59 +00:00
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# sunflower_data,sunflower_result = get_word_pairs_data('sweater',15)
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# print(np.argmax(model.predict([sunflower_data[:, 0], sunflower_data[:, 1]]),axis=1))
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# print(sunflower_result)
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