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import unittest |
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import torch |
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from examples.speech_recognition.data import data_utils |
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class DataUtilsTest(unittest.TestCase): |
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def test_normalization(self): |
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sample_len1 = torch.tensor( |
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[ |
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[ |
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-0.7661, |
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-1.3889, |
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-2.0972, |
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-0.9134, |
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-0.7071, |
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-0.9765, |
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-0.8700, |
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-0.8283, |
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0.7512, |
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1.3211, |
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2.1532, |
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2.1174, |
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1.2800, |
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1.2633, |
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1.6147, |
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1.6322, |
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2.0723, |
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3.1522, |
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3.2852, |
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2.2309, |
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2.5569, |
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2.2183, |
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2.2862, |
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1.5886, |
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0.8773, |
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0.8725, |
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1.2662, |
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0.9899, |
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1.1069, |
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1.3926, |
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1.2795, |
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1.1199, |
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1.1477, |
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1.2687, |
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1.3843, |
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1.1903, |
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0.8355, |
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1.1367, |
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1.2639, |
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1.4707, |
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] |
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] |
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) |
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out = data_utils.apply_mv_norm(sample_len1) |
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assert not torch.isnan(out).any() |
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assert (out == sample_len1).all() |
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