diff options
Diffstat (limited to 'third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/annotations_unittest.py')
| -rw-r--r-- | third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/annotations_unittest.py | 160 |
1 files changed, 160 insertions, 0 deletions
diff --git a/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/annotations_unittest.py b/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/annotations_unittest.py new file mode 100644 index 0000000000..8230208808 --- /dev/null +++ b/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/annotations_unittest.py @@ -0,0 +1,160 @@ +# Copyright (c) 2017 The WebRTC project authors. All Rights Reserved. +# +# Use of this source code is governed by a BSD-style license +# that can be found in the LICENSE file in the root of the source +# tree. An additional intellectual property rights grant can be found +# in the file PATENTS. All contributing project authors may +# be found in the AUTHORS file in the root of the source tree. +"""Unit tests for the annotations module. +""" + +from __future__ import division +import logging +import os +import shutil +import tempfile +import unittest + +import numpy as np + +from . import annotations +from . import external_vad +from . import input_signal_creator +from . import signal_processing + + +class TestAnnotationsExtraction(unittest.TestCase): + """Unit tests for the annotations module. + """ + + _CLEAN_TMP_OUTPUT = True + _DEBUG_PLOT_VAD = False + _VAD_TYPE_CLASS = annotations.AudioAnnotationsExtractor.VadType + _ALL_VAD_TYPES = (_VAD_TYPE_CLASS.ENERGY_THRESHOLD + | _VAD_TYPE_CLASS.WEBRTC_COMMON_AUDIO + | _VAD_TYPE_CLASS.WEBRTC_APM) + + def setUp(self): + """Create temporary folder.""" + self._tmp_path = tempfile.mkdtemp() + self._wav_file_path = os.path.join(self._tmp_path, 'tone.wav') + pure_tone, _ = input_signal_creator.InputSignalCreator.Create( + 'pure_tone', [440, 1000]) + signal_processing.SignalProcessingUtils.SaveWav( + self._wav_file_path, pure_tone) + self._sample_rate = pure_tone.frame_rate + + def tearDown(self): + """Recursively delete temporary folder.""" + if self._CLEAN_TMP_OUTPUT: + shutil.rmtree(self._tmp_path) + else: + logging.warning(self.id() + ' did not clean the temporary path ' + + (self._tmp_path)) + + def testFrameSizes(self): + e = annotations.AudioAnnotationsExtractor(self._ALL_VAD_TYPES) + e.Extract(self._wav_file_path) + samples_to_ms = lambda n, sr: 1000 * n // sr + self.assertEqual( + samples_to_ms(e.GetLevelFrameSize(), self._sample_rate), + e.GetLevelFrameSizeMs()) + self.assertEqual(samples_to_ms(e.GetVadFrameSize(), self._sample_rate), + e.GetVadFrameSizeMs()) + + def testVoiceActivityDetectors(self): + for vad_type_value in range(0, self._ALL_VAD_TYPES + 1): + vad_type = self._VAD_TYPE_CLASS(vad_type_value) + e = annotations.AudioAnnotationsExtractor(vad_type=vad_type_value) + e.Extract(self._wav_file_path) + if vad_type.Contains(self._VAD_TYPE_CLASS.ENERGY_THRESHOLD): + # pylint: disable=unpacking-non-sequence + vad_output = e.GetVadOutput( + self._VAD_TYPE_CLASS.ENERGY_THRESHOLD) + self.assertGreater(len(vad_output), 0) + self.assertGreaterEqual( + float(np.sum(vad_output)) / len(vad_output), 0.95) + + if vad_type.Contains(self._VAD_TYPE_CLASS.WEBRTC_COMMON_AUDIO): + # pylint: disable=unpacking-non-sequence + vad_output = e.GetVadOutput( + self._VAD_TYPE_CLASS.WEBRTC_COMMON_AUDIO) + self.assertGreater(len(vad_output), 0) + self.assertGreaterEqual( + float(np.sum(vad_output)) / len(vad_output), 0.95) + + if vad_type.Contains(self._VAD_TYPE_CLASS.WEBRTC_APM): + # pylint: disable=unpacking-non-sequence + (vad_probs, + vad_rms) = e.GetVadOutput(self._VAD_TYPE_CLASS.WEBRTC_APM) + self.assertGreater(len(vad_probs), 0) + self.assertGreater(len(vad_rms), 0) + self.assertGreaterEqual( + float(np.sum(vad_probs)) / len(vad_probs), 0.5) + self.assertGreaterEqual( + float(np.sum(vad_rms)) / len(vad_rms), 20000) + + if self._DEBUG_PLOT_VAD: + frame_times_s = lambda num_frames, frame_size_ms: np.arange( + num_frames).astype(np.float32) * frame_size_ms / 1000.0 + level = e.GetLevel() + t_level = frame_times_s(num_frames=len(level), + frame_size_ms=e.GetLevelFrameSizeMs()) + t_vad = frame_times_s(num_frames=len(vad_output), + frame_size_ms=e.GetVadFrameSizeMs()) + import matplotlib.pyplot as plt + plt.figure() + plt.hold(True) + plt.plot(t_level, level) + plt.plot(t_vad, vad_output * np.max(level), '.') + plt.show() + + def testSaveLoad(self): + e = annotations.AudioAnnotationsExtractor(self._ALL_VAD_TYPES) + e.Extract(self._wav_file_path) + e.Save(self._tmp_path, "fake-annotation") + + data = np.load( + os.path.join( + self._tmp_path, + e.GetOutputFileNameTemplate().format("fake-annotation"))) + np.testing.assert_array_equal(e.GetLevel(), data['level']) + self.assertEqual(np.float32, data['level'].dtype) + np.testing.assert_array_equal( + e.GetVadOutput(self._VAD_TYPE_CLASS.ENERGY_THRESHOLD), + data['vad_energy_output']) + np.testing.assert_array_equal( + e.GetVadOutput(self._VAD_TYPE_CLASS.WEBRTC_COMMON_AUDIO), + data['vad_output']) + np.testing.assert_array_equal( + e.GetVadOutput(self._VAD_TYPE_CLASS.WEBRTC_APM)[0], + data['vad_probs']) + np.testing.assert_array_equal( + e.GetVadOutput(self._VAD_TYPE_CLASS.WEBRTC_APM)[1], + data['vad_rms']) + self.assertEqual(np.uint8, data['vad_energy_output'].dtype) + self.assertEqual(np.float64, data['vad_probs'].dtype) + self.assertEqual(np.float64, data['vad_rms'].dtype) + + def testEmptyExternalShouldNotCrash(self): + for vad_type_value in range(0, self._ALL_VAD_TYPES + 1): + annotations.AudioAnnotationsExtractor(vad_type_value, {}) + + def testFakeExternalSaveLoad(self): + def FakeExternalFactory(): + return external_vad.ExternalVad( + os.path.join(os.path.dirname(os.path.abspath(__file__)), + 'fake_external_vad.py'), 'fake') + + for vad_type_value in range(0, self._ALL_VAD_TYPES + 1): + e = annotations.AudioAnnotationsExtractor( + vad_type_value, {'fake': FakeExternalFactory()}) + e.Extract(self._wav_file_path) + e.Save(self._tmp_path, annotation_name="fake-annotation") + data = np.load( + os.path.join( + self._tmp_path, + e.GetOutputFileNameTemplate().format("fake-annotation"))) + self.assertEqual(np.float32, data['extvad_conf-fake'].dtype) + np.testing.assert_almost_equal(np.arange(100, dtype=np.float32), + data['extvad_conf-fake']) |