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| # Copyright 2019 The TensorFlow Authors All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # ============================================================================== | |
| """Installation test for YAMNet.""" | |
| import numpy as np | |
| import tensorflow as tf | |
| import params | |
| import yamnet | |
| class YAMNetTest(tf.test.TestCase): | |
| _yamnet_graph = None | |
| _yamnet = None | |
| _yamnet_classes = None | |
| def setUpClass(cls): | |
| super(YAMNetTest, cls).setUpClass() | |
| cls._yamnet_graph = tf.Graph() | |
| with cls._yamnet_graph.as_default(): | |
| cls._yamnet = yamnet.yamnet_frames_model(params) | |
| cls._yamnet.load_weights('yamnet.h5') | |
| cls._yamnet_classes = yamnet.class_names('yamnet_class_map.csv') | |
| def clip_test(self, waveform, expected_class_name, top_n=10): | |
| """Run the model on the waveform, check that expected class is in top-n.""" | |
| with YAMNetTest._yamnet_graph.as_default(): | |
| prediction = np.mean(YAMNetTest._yamnet.predict( | |
| np.reshape(waveform, [1, -1]), steps=1)[0], axis=0) | |
| top_n_class_names = YAMNetTest._yamnet_classes[ | |
| np.argsort(prediction)[-top_n:]] | |
| self.assertIn(expected_class_name, top_n_class_names) | |
| def testZeros(self): | |
| self.clip_test( | |
| waveform=np.zeros((1, int(3 * params.SAMPLE_RATE))), | |
| expected_class_name='Silence') | |
| def testRandom(self): | |
| np.random.seed(51773) # Ensure repeatability. | |
| self.clip_test( | |
| waveform=np.random.uniform(-1.0, +1.0, | |
| (1, int(3 * params.SAMPLE_RATE))), | |
| expected_class_name='White noise') | |
| def testSine(self): | |
| self.clip_test( | |
| waveform=np.reshape( | |
| np.sin(2 * np.pi * 440 * np.linspace( | |
| 0, 3, int(3 *params.SAMPLE_RATE))), | |
| [1, -1]), | |
| expected_class_name='Sine wave') | |
| if __name__ == '__main__': | |
| tf.test.main() | |