314 lines
13 KiB
Python
314 lines
13 KiB
Python
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# Lint as: python2, python3
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# Copyright 2021 The Chromium OS Authors. All rights reserved.
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# Use of this source code is governed by a BSD-style license that can be
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# found in the LICENSE file.
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"""This is a server side noise cancellation test using the Chameleon board."""
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import logging
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import os
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import time
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from autotest_lib.client.common_lib import error
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from autotest_lib.client.cros.audio import audio_test_data
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from autotest_lib.client.cros.audio import sox_utils
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from autotest_lib.client.cros.audio import visqol_utils
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from autotest_lib.client.cros.bluetooth.bluetooth_audio_test_data import (
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download_file_from_bucket, get_visqol_binary)
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from autotest_lib.client.cros.chameleon import audio_test_utils
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from autotest_lib.client.cros.chameleon import chameleon_audio_ids
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from autotest_lib.client.cros.chameleon import chameleon_audio_helper
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from autotest_lib.server.cros.audio import audio_test
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DIST_FILES_DIR = 'gs://chromeos-localmirror/distfiles/test_noise_cancellation'
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DATA_DIR = '/tmp'
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# Verification steps for the Noise Cancellation processing (NC):
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# 1. Prepare the audio source file and reference file.
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# 2. Play the source file by Chameleon.
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# 3. Record by DUT Internal Mic when NC is on and get ViSQOL score A.
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# 4. Repeat step 2.
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# 5. Record by DUT Internal Mic when NC is off and get ViSQOL score B.
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# 6. Check if A - B >= threshold
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#
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# In practice, ViSQOL is not the most suitable metrics for NC due to its
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# intrusive design (reference: go/visqol). However, it is fair enough to compare
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# the relative gain (or degradation) between before and after de-noising.
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#
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# TODO(johnylin): replace ViSQOL with other metrics if applicable.
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# TODO(johnylin): add more speech and noise test inputs for inclusion.
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class audio_AudioNoiseCancellation(audio_test.AudioTest):
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"""Server side input audio noise cancellation test.
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This test talks to a Chameleon board and a Cros device to verify
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input audio noise cancellation function of the Cros device.
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"""
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version = 1
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DELAY_BEFORE_PLAYBACK_SECONDS = 3.0
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DELAY_AFTER_PLAYBACK_SECONDS = 2.0
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DELAY_AFTER_BINDING = 0.5
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DELAY_AFTER_NC_TOGGLED = 0.5
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cleanup_files = []
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def cleanup(self):
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# Restore the default state of bypass blocking mechanism in Cras.
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# Restarting Cras is only way because we are not able to know the
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# default state.
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self.host.run('restart cras')
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# Start Chrome UI.
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self.host.run('start ui')
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# Remove downloaded files and the temporary generated files.
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for cleanup_file in self.cleanup_files:
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if os.path.isfile(cleanup_file):
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os.remove(cleanup_file)
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def download_file_from_bucket(self, file):
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"""Download the file from GS bucket.
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@param file: the file name for download.
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@raises: error.TestError if failed.
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@returns: the local path of the downloaded file.
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"""
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remote_path = os.path.join(DIST_FILES_DIR, file)
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if not download_file_from_bucket(
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DATA_DIR, remote_path, lambda _, __, p: p.returncode == 0):
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logging.error('Failed to download %s to %s', remote_path, DATA_DIR)
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raise error.TestError('Failed to download file %s from bucket.' %
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file)
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return os.path.join(DATA_DIR, file)
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def generate_noisy_speech_file(self, speech_path, noise_path):
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"""Generate the mixed audio file of speech and noise data.
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@param speech_path: the file path of the pure speech audio.
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@param noise_path: the file path of the noise audio.
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@raises: error.TestError if failed.
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@returns: the file path of the mixed audio.
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"""
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mixed_wav_path = os.path.join(DATA_DIR, 'speech_noise_mixed.wav')
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if os.path.exists(mixed_wav_path):
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os.remove(mixed_wav_path)
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sox_utils.mix_two_wav_files(speech_path,
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noise_path,
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mixed_wav_path,
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input_volume=1.0)
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if not os.path.isfile(mixed_wav_path):
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logging.error('WAV file %s does not exist.', mixed_wav_path)
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raise error.TestError('Failed to mix %s and %s by sox commands.' %
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(speech_path, noise_path))
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return mixed_wav_path
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def run_once(self, test_data):
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"""Runs Audio Noise Cancellation test.
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Test scenarios can be distinguished by the elements (keys) in test_data.
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Noisy environment test:
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test_data = dict(
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speech_file: the WAV file for the pure speech data.
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noise_file: the WAV file for the noise data.
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threshold: the min required score gain for NC effect.)
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Quiet environment test:
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test_data = dict(
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speech_file: the WAV file for the pure speech data.
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threshold: the min score diff tolerance for NC effect.)
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@param test_data: the dict for files and threshold as mentioned above.
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"""
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if not self.facade.get_noise_cancellation_supported():
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logging.warning('Noise Cancellation is not supported.')
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raise error.TestWarn('Noise Cancellation is not supported.')
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def _remove_at_cleanup(filepath):
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self.cleanup_files.append(filepath)
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# Download the files from bucket.
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speech_path = self.download_file_from_bucket(test_data['speech_file'])
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_remove_at_cleanup(speech_path)
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ref_infos = sox_utils.get_infos_from_wav_file(speech_path)
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if ref_infos is None:
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raise error.TestError('Failed to get infos from wav file %s.' %
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speech_path)
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if 'noise_file' in test_data:
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# Noisy environment test when 'noise_file' is given.
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noise_path = self.download_file_from_bucket(
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test_data['noise_file'])
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_remove_at_cleanup(noise_path)
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test_audio_path = self.generate_noisy_speech_file(
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speech_path, noise_path)
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_remove_at_cleanup(test_audio_path)
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test_infos = sox_utils.get_infos_from_wav_file(test_audio_path)
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if test_infos is None:
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raise error.TestError('Failed to get infos from wav file %s.' %
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test_audio_path)
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else:
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# Quiet environment test.
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test_audio_path = speech_path
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test_infos = ref_infos
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playback_testdata = audio_test_data.AudioTestData(
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path=test_audio_path,
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data_format=dict(file_type='wav',
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sample_format='S{}_LE'.format(
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test_infos['bits']),
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channel=test_infos['channels'],
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rate=test_infos['rate']),
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duration_secs=test_infos['duration'])
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# Get and set VISQOL working environment.
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get_visqol_binary()
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# Bypass blocking mechanism in Cras to make sure Noise Cancellation is
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# enabled.
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self.facade.set_bypass_block_noise_cancellation(bypass=True)
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source = self.widget_factory.create_widget(
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chameleon_audio_ids.ChameleonIds.LINEOUT)
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sink = self.widget_factory.create_widget(
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chameleon_audio_ids.PeripheralIds.SPEAKER)
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binder = self.widget_factory.create_binder(source, sink)
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recorder = self.widget_factory.create_widget(
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chameleon_audio_ids.CrosIds.INTERNAL_MIC)
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# Select and check the node selected by cras is correct.
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audio_test_utils.check_and_set_chrome_active_node_types(
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self.facade, None,
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audio_test_utils.get_internal_mic_node(self.host))
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# Adjust the proper input gain.
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self.facade.set_chrome_active_input_gain(50)
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# Stop Chrome UI to avoid NC state preference intervened by Chrome.
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self.host.run('stop ui')
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logging.info(
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'UI is stopped to avoid NC preference intervention from Chrome'
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)
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def _run_routine(recorded_filename, nc_enabled):
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# Set NC state via D-Bus control.
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self.facade.set_noise_cancellation_enabled(nc_enabled)
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time.sleep(self.DELAY_AFTER_NC_TOGGLED)
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with chameleon_audio_helper.bind_widgets(binder):
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time.sleep(self.DELAY_AFTER_BINDING)
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logfile_suffix = 'nc_on' if nc_enabled else 'nc_off'
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audio_test_utils.dump_cros_audio_logs(
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self.host, self.facade, self.resultsdir,
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'after_binding.{}'.format(logfile_suffix))
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logging.info('Set playback data on Chameleon')
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source.set_playback_data(playback_testdata)
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# Start recording, wait a few seconds, and then start playback.
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# Make sure the recorded data has silent samples in the
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# beginning to trim, and includes the entire playback content.
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logging.info('Start recording from Cros device')
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recorder.start_recording()
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time.sleep(self.DELAY_BEFORE_PLAYBACK_SECONDS)
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logging.info('Start playing %s from Chameleon',
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playback_testdata.path)
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source.start_playback()
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time.sleep(test_infos['duration'] +
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self.DELAY_AFTER_PLAYBACK_SECONDS)
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recorder.stop_recording()
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logging.info('Stopped recording from Cros device.')
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audio_test_utils.dump_cros_audio_logs(
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self.host, self.facade, self.resultsdir,
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'after_recording.{}'.format(logfile_suffix))
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recorder.read_recorded_binary()
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logging.info('Read recorded binary from Cros device.')
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# Remove the beginning of recorded data. This is to avoid artifact
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# caused by Cros device codec initialization in the beginning of
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# recording.
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recorder.remove_head(1.0)
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recorded_file = os.path.join(self.resultsdir,
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recorded_filename + '.raw')
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logging.info('Saving recorded data to %s', recorded_file)
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recorder.save_file(recorded_file)
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_remove_at_cleanup(recorded_file)
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# WAV file is also saved by recorder.save_file().
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recorded_wav_path = recorded_file + '.wav'
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if not os.path.isfile(recorded_wav_path):
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logging.error('WAV file %s does not exist.', recorded_wav_path)
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raise error.TestError('Failed to find recorded wav file.')
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_remove_at_cleanup(recorded_wav_path)
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rec_infos = sox_utils.get_infos_from_wav_file(recorded_wav_path)
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if rec_infos is None:
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raise error.TestError('Failed to get infos from wav file %s.' %
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recorded_wav_path)
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# Downsample the recorded data from 48k to 16k rate. It is required
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# for getting ViSQOL score in speech mode.
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recorded_16k_path = '{}_16k{}'.format(
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*os.path.splitext(recorded_wav_path))
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sox_utils.convert_format(recorded_wav_path,
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rec_infos['channels'],
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rec_infos['bits'],
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rec_infos['rate'],
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recorded_16k_path,
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ref_infos['channels'],
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ref_infos['bits'],
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ref_infos['rate'],
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1.0,
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use_src_header=True,
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use_dst_header=True)
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# Remove the silence in the beginning and trim to the same duration
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# as the reference file.
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trimmed_recorded_16k_path = '{}_trim{}'.format(
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*os.path.splitext(recorded_16k_path))
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sox_utils.trim_silence_from_wav_file(recorded_16k_path,
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trimmed_recorded_16k_path,
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ref_infos['duration'],
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duration_threshold=0.05)
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score = visqol_utils.get_visqol_score(
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ref_file=speech_path,
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deg_file=trimmed_recorded_16k_path,
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log_dir=self.resultsdir,
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speech_mode=True)
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logging.info('Recorded audio %s got ViSQOL score: %f',
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recorded_filename, score)
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return score
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logging.info('Run routine with NC enabled...')
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nc_on_score = _run_routine('record_nc_enabled', nc_enabled=True)
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logging.info('Run routine with NC disabled...')
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nc_off_score = _run_routine('record_nc_disabled', nc_enabled=False)
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score_diff = nc_on_score - nc_off_score
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# Track ViSQOL performance score
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test_desc = 'internal_mic_noise_cancellation_visqol_diff'
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self.write_perf_keyval({test_desc: score_diff})
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if score_diff < test_data['threshold']:
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raise error.TestFail(
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'ViSQOL score diff for NC(=%f) is lower than threshold(=%f)'
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% (score_diff, test_data['threshold']))
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