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speech-scoring
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perfect score on new test words - TODO evaluate on real voice

master
Malar Kannan 2017-11-16 14:19:25 +05:30
parent 7d94ddc2ae
commit 3d297f176f
1 changed files with 4 additions and 4 deletions
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speech_test.py
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@ -130,7 +130,7 @@ def play_results(audio_group='audio'):
def visualize_results(audio_group='audio'): def visualize_results(audio_group='audio'):
# %matplotlib inline # %matplotlib inline
audio_group = 'story_words' audio_group = 'story_words.gpu'
result = pd.read_csv('./outputs/' + audio_group + '.results.csv',index_col=0) result = pd.read_csv('./outputs/' + audio_group + '.results.csv',index_col=0)
result.groupby('success').size().plot(kind='bar') result.groupby('success').size().plot(kind='bar')
result.describe(include=['object']) result.describe(include=['object'])
@ -139,13 +139,13 @@ def visualize_results(audio_group='audio'):
diff_failed = failed[failed['variant1'] != failed['variant2']] diff_failed = failed[failed['variant1'] != failed['variant2']]
same_failed[same_failed['voice1'] != same_failed['voice2']] same_failed[same_failed['voice1'] != same_failed['voice2']]
diff_failed[diff_failed['voice1'] != diff_failed['voice2']] diff_failed[diff_failed['voice1'] != diff_failed['voice2']]
if __name__ == '__main__': if __name__ == '__main__':
# evaluate_siamese('./outputs/story_words_test.train.tfrecords',audio_group='story_words',weights ='siamese_speech_model-712-epoch-0.00-acc.h5') # evaluate_siamese('./outputs/story_words_test.train.tfrecords',audio_group='story_words.gpu',weights ='siamese_speech_model-58-epoch-0.00-acc.h5')
# evaluate_siamese('./outputs/story_words.test.tfrecords',audio_group='story_words',weights ='siamese_speech_model-675-epoch-0.00-acc.h5') # evaluate_siamese('./outputs/story_words.test.tfrecords',audio_group='story_words',weights ='siamese_speech_model-675-epoch-0.00-acc.h5')
# play_results('story_words') # play_results('story_words')
visualize_results('story_words') visualize_results('story_words.gpu')
# test_with('rand_edu') # test_with('rand_edu')
# sunflower_data,sunflower_result = get_word_pairs_data('sweater',15) # sunflower_data,sunflower_result = get_word_pairs_data('sweater',15)
# print(np.argmax(model.predict([sunflower_data[:, 0], sunflower_data[:, 1]]),axis=1)) # print(np.argmax(model.predict([sunflower_data[:, 0], sunflower_data[:, 1]]),axis=1))