1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
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'])
|
