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Summary: Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1357 Reviewed By: alexeib Differential Revision: D24377772 fbshipit-source-id: 51581af041d42d62166b33a35a1a4228b1a76f0c
63 lines
1.7 KiB
Python
63 lines
1.7 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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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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