Total Harmonic Distorsion plus noise (THD+n) score in APM-QA.
In order to compute a THD score, a pure tone must be used as input signal. Also, its frequency must be known. For this reason, this CL adds a number of changes in the APM-QA pipeline. More in detail, input signal metadata is loaded and passed to the THD evaluation score instance. This makes the eval_scores module less reusable, but it is fine since the module has been specifically designed for the APM-QA module. BUG=webrtc:7494 Review-Url: https://codereview.webrtc.org/3010413002 Cr-Commit-Position: refs/heads/master@{#19970}
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@ -33,6 +33,12 @@ reference one used for evaluation.
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- Go to `out/Default/py_quality_assessment` and check that
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`apm_quality_assessment.py` exists
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## Unit tests
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- Compile WebRTC
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- Go to `out/Default/py_quality_assessment`
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- Run `python -m unittest -p "*_unittest.py" discover`
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## First time setup
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- Deploy PolqaOem64 and set the `POLQA_PATH` environment variable
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@ -31,8 +31,33 @@ class Metadata(object):
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def __init__(self):
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pass
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_GENERIC_METADATA_SUFFIX = '.mdata'
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_AUDIO_TEST_DATA_FILENAME = 'audio_test_data.json'
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@classmethod
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def LoadFileMetadata(cls, filepath):
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"""Loads generic metadata linked to a file.
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Args:
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filepath: path to the metadata file to read.
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Returns:
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A dict.
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"""
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with open(filepath + cls._GENERIC_METADATA_SUFFIX) as f:
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return json.load(f)
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@classmethod
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def SaveFileMetadata(cls, filepath, metadata):
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"""Saves generic metadata linked to a file.
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Args:
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filepath: path to the metadata file to write.
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metadata: a dict.
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"""
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with open(filepath + cls._GENERIC_METADATA_SUFFIX, 'w') as f:
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json.dump(metadata, f)
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@classmethod
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def LoadAudioTestDataPaths(cls, metadata_path):
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"""Loads the input and the reference audio track paths.
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@ -14,6 +14,13 @@ import logging
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import os
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import re
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import subprocess
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import sys
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try:
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import numpy as np
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except ImportError:
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logging.critical('Cannot import the third-party Python package numpy')
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sys.exit(1)
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from . import data_access
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from . import exceptions
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@ -27,6 +34,7 @@ class EvaluationScore(object):
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def __init__(self, score_filename_prefix):
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self._score_filename_prefix = score_filename_prefix
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self._input_signal_metadata = None
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self._reference_signal = None
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self._reference_signal_filepath = None
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self._tested_signal = None
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@ -56,8 +64,16 @@ class EvaluationScore(object):
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def score(self):
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return self._score
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def SetInputSignalMetadata(self, metadata):
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"""Sets input signal metadata.
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Args:
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metadata: dict instance.
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"""
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self._input_signal_metadata = metadata
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def SetReferenceSignalFilepath(self, filepath):
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""" Sets the path to the audio track used as reference signal.
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"""Sets the path to the audio track used as reference signal.
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Args:
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filepath: path to the reference audio track.
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@ -65,7 +81,7 @@ class EvaluationScore(object):
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self._reference_signal_filepath = filepath
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def SetTestedSignalFilepath(self, filepath):
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""" Sets the path to the audio track used as test signal.
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"""Sets the path to the audio track used as test signal.
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Args:
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filepath: path to the test audio track.
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@ -242,3 +258,84 @@ class PolqaScore(EvaluationScore):
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# Build and return a dictionary with field names (header) as keys and the
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# corresponding field values as values.
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return {data[0][index]: data[1][index] for index in range(number_of_fields)}
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@EvaluationScore.RegisterClass
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class TotalHarmonicDistorsionScore(EvaluationScore):
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"""Total harmonic distorsion plus noise score.
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Total harmonic distorsion plus noise score.
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See "https://en.wikipedia.org/wiki/Total_harmonic_distortion#THD.2BN".
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Unit: -.
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Ideal: 0.
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Worst case: +inf
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"""
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NAME = 'thd'
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def __init__(self, score_filename_prefix):
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EvaluationScore.__init__(self, score_filename_prefix)
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self._input_frequency = None
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def _Run(self, output_path):
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# TODO(aleloi): Integrate changes made locally.
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self._CheckInputSignal()
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self._LoadTestedSignal()
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if self._tested_signal.channels != 1:
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raise exceptions.EvaluationScoreException(
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'unsupported number of channels')
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samples = signal_processing.SignalProcessingUtils.AudioSegmentToRawData(
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self._tested_signal)
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# Init.
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num_samples = len(samples)
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duration = len(self._tested_signal) / 1000.0
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scaling = 2.0 / num_samples
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max_freq = self._tested_signal.frame_rate / 2
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f0_freq = float(self._input_frequency)
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t = np.linspace(0, duration, num_samples)
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# Analyze harmonics.
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b_terms = []
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n = 1
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while f0_freq * n < max_freq:
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x_n = np.sum(samples * np.sin(2.0 * np.pi * n * f0_freq * t)) * scaling
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y_n = np.sum(samples * np.cos(2.0 * np.pi * n * f0_freq * t)) * scaling
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b_terms.append(np.sqrt(x_n**2 + y_n**2))
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n += 1
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output_without_fundamental = samples - b_terms[0] * np.sin(
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2.0 * np.pi * f0_freq * t)
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distortion_and_noise = np.sqrt(np.sum(
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output_without_fundamental**2) * np.pi * scaling)
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# TODO(alessiob): Fix or remove if not needed.
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# thd = np.sqrt(np.sum(b_terms[1:]**2)) / b_terms[0]
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# TODO(alessiob): Check the range of |thd_plus_noise| and update the class
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# docstring above if accordingly.
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thd_plus_noise = distortion_and_noise / b_terms[0]
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self._score = thd_plus_noise
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self._SaveScore()
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def _CheckInputSignal(self):
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# Check input signal and get properties.
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try:
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if self._input_signal_metadata['signal'] != 'pure_tone':
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raise exceptions.EvaluationScoreException(
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'The THD score requires a pure tone as input signal')
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self._input_frequency = self._input_signal_metadata['frequency']
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if self._input_signal_metadata['test_data_gen_name'] != 'identity' or (
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self._input_signal_metadata['test_data_gen_config'] != 'default'):
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raise exceptions.EvaluationScoreException(
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'The THD score cannot be used with any test data generator other '
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'than "identity"')
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except TypeError:
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raise exceptions.EvaluationScoreException(
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'The THD score requires an input signal with associated metadata')
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except KeyError:
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raise exceptions.EvaluationScoreException(
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'Invalid input signal metadata to compute the THD score')
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@ -52,6 +52,9 @@ class TestEvalScores(unittest.TestCase):
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shutil.rmtree(self._output_path)
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def testRegisteredClasses(self):
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# Evaluation score names to exclude (tested separately).
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exceptions = ['thd']
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# Preliminary check.
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self.assertTrue(os.path.exists(self._output_path))
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@ -69,11 +72,14 @@ class TestEvalScores(unittest.TestCase):
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# Try each registered evaluation score worker.
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for eval_score_name in registered_classes:
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if eval_score_name in exceptions:
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continue
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# Instance evaluation score worker.
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eval_score_worker = eval_score_workers_factory.GetInstance(
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registered_classes[eval_score_name])
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# Set reference and test, then run.
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# Set fake input metadata and reference and test file paths, then run.
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eval_score_worker.SetReferenceSignalFilepath(
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self._fake_reference_signal_filepath)
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eval_score_worker.SetTestedSignalFilepath(
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@ -83,3 +89,43 @@ class TestEvalScores(unittest.TestCase):
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# Check output.
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score = data_access.ScoreFile.Load(eval_score_worker.output_filepath)
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self.assertTrue(isinstance(score, float))
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def testTotalHarmonicDistorsionScore(self):
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# Init.
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pure_tone_freq = 5000.0
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eval_score_worker = eval_scores.TotalHarmonicDistorsionScore('scores-')
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eval_score_worker.SetInputSignalMetadata({
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'signal': 'pure_tone',
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'frequency': pure_tone_freq,
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'test_data_gen_name': 'identity',
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'test_data_gen_config': 'default',
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})
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template = pydub.AudioSegment.silent(duration=1000, frame_rate=48000)
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# Create 3 test signals: pure tone, pure tone + white noise, white noise
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# only.
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pure_tone = signal_processing.SignalProcessingUtils.GeneratePureTone(
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template, pure_tone_freq)
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white_noise = signal_processing.SignalProcessingUtils.GenerateWhiteNoise(
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template)
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noisy_tone = signal_processing.SignalProcessingUtils.MixSignals(
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pure_tone, white_noise)
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# Compute scores for increasingly distorted pure tone signals.
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scores = [None, None, None]
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for index, tested_signal in enumerate([pure_tone, noisy_tone, white_noise]):
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# Save signal.
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tmp_filepath = os.path.join(self._output_path, 'tmp_thd.wav')
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signal_processing.SignalProcessingUtils.SaveWav(
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tmp_filepath, tested_signal)
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# Compute score.
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eval_score_worker.SetTestedSignalFilepath(tmp_filepath)
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eval_score_worker.Run(self._output_path)
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scores[index] = eval_score_worker.score
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# Remove output file to avoid caching.
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os.remove(eval_score_worker.output_filepath)
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# Validate scores (lowest score with a pure tone).
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self.assertTrue(all([scores[i + 1] > scores[i] for i in range(2)]))
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@ -20,14 +20,15 @@ class ApmModuleEvaluator(object):
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pass
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@classmethod
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def Run(cls, evaluation_score_workers, apm_output_filepath,
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reference_input_filepath, output_path):
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def Run(cls, evaluation_score_workers, apm_input_metadata,
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apm_output_filepath, reference_input_filepath, output_path):
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"""Runs the evaluation.
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Iterates over the given evaluation score workers.
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Args:
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evaluation_score_workers: list of EvaluationScore instances.
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apm_input_metadata: dictionary with metadata of the APM input.
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apm_output_filepath: path to the audio track file with the APM output.
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reference_input_filepath: path to the reference audio track file.
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output_path: output path.
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@ -40,6 +41,7 @@ class ApmModuleEvaluator(object):
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for evaluation_score_worker in evaluation_score_workers:
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logging.info(' computing <%s> score', evaluation_score_worker.NAME)
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evaluation_score_worker.SetInputSignalMetadata(apm_input_metadata)
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evaluation_score_worker.SetReferenceSignalFilepath(
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reference_input_filepath)
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evaluation_score_worker.SetTestedSignalFilepath(
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@ -32,3 +32,9 @@ class InputSignalCreatorException(Exception):
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"""Input signal creator exeception.
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"""
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pass
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class EvaluationScoreException(Exception):
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"""Evaluation score exeception.
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"""
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pass
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@ -18,26 +18,36 @@ class InputSignalCreator(object):
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"""
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@classmethod
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def Create(cls, name, params):
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"""Creates a input signal.
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def Create(cls, name, raw_params):
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"""Creates a input signal and its metadata.
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Args:
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name: Input signal creator name.
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params: Tuple of parameters to pass to the specific signal creator.
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raw_params: Tuple of parameters to pass to the specific signal creator.
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Returns:
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AudioSegment instance.
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(AudioSegment, dict) tuple.
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"""
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try:
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signal = {}
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params = {}
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if name == 'pure_tone':
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return cls._CreatePureTone(float(params[0]), int(params[1]))
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params['frequency'] = float(raw_params[0])
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params['duration'] = int(raw_params[1])
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signal = cls._CreatePureTone(params['frequency'], params['duration'])
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else:
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raise exceptions.InputSignalCreatorException(
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'Invalid input signal creator name')
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# Complete metadata.
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params['signal'] = name
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return signal, params
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except (TypeError, AssertionError) as e:
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raise exceptions.InputSignalCreatorException(
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'Invalid signal creator parameters: {}'.format(e))
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raise exceptions.InputSignalCreatorException(
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'Invalid input signal creator name')
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@classmethod
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def _CreatePureTone(cls, frequency, duration):
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"""
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@ -148,6 +148,13 @@ class SignalProcessingUtils(object):
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duration=len(template),
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volume=0.0)
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@classmethod
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def AudioSegmentToRawData(cls, signal):
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samples = signal.get_array_of_samples()
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if samples.typecode != 'h':
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raise exceptions.SignalProcessingException('Unsupported samples type')
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return np.array(signal.get_array_of_samples(), np.int16)
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@classmethod
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def DetectHardClipping(cls, signal, threshold=2):
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"""Detects hard clipping.
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@ -169,13 +176,7 @@ class SignalProcessingUtils(object):
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if signal.sample_width != 2: # Note that signal.sample_width is in bytes.
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raise exceptions.SignalProcessingException(
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'hard-clipping detection only supported for 16 bit samples')
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# Get raw samples, check type, cast.
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samples = signal.get_array_of_samples()
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if samples.typecode != 'h':
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raise exceptions.SignalProcessingException(
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'hard-clipping detection only supported for 16 bit samples')
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samples = np.array(signal.get_array_of_samples(), np.int16)
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samples = cls.AudioSegmentToRawData(signal)
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# Detect adjacent clipped samples.
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samples_type_info = np.iinfo(samples.dtype)
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@ -17,6 +17,7 @@ from . import echo_path_simulation
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from . import echo_path_simulation_factory
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from . import eval_scores
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from . import eval_scores_factory
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from . import exceptions
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from . import input_mixer
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from . import test_data_generation
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from . import test_data_generation_factory
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@ -248,9 +249,20 @@ class ApmModuleSimulator(object):
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test_data_cache_path=test_data_cache_path,
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base_output_path=output_path)
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# Extract metadata linked to the clean input file (if any).
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apm_input_metadata = None
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try:
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apm_input_metadata = data_access.Metadata.LoadFileMetadata(
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clean_capture_input_filepath)
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except IOError as e:
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apm_input_metadata = {}
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apm_input_metadata['test_data_gen_name'] = test_data_generators.NAME
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apm_input_metadata['test_data_gen_config'] = None
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# For each test data pair, simulate a call and evaluate.
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for config_name in test_data_generators.config_names:
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logging.info(' - test data generator config: <%s>', config_name)
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apm_input_metadata['test_data_gen_config'] = config_name
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# Paths to the test data generator output.
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# Note that the reference signal does not depend on the render input
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@ -278,23 +290,28 @@ class ApmModuleSimulator(object):
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render_input_filepath=render_input_filepath,
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output_path=evaluation_output_path)
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# Evaluate.
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self._evaluator.Run(
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evaluation_score_workers=self._evaluation_score_workers,
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apm_output_filepath=self._audioproc_wrapper.output_filepath,
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reference_input_filepath=reference_signal_filepath,
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output_path=evaluation_output_path)
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try:
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# Evaluate.
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self._evaluator.Run(
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evaluation_score_workers=self._evaluation_score_workers,
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apm_input_metadata=apm_input_metadata,
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apm_output_filepath=self._audioproc_wrapper.output_filepath,
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reference_input_filepath=reference_signal_filepath,
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output_path=evaluation_output_path)
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# Save simulation metadata.
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data_access.Metadata.SaveAudioTestDataPaths(
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output_path=evaluation_output_path,
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clean_capture_input_filepath=clean_capture_input_filepath,
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echo_free_capture_filepath=noisy_capture_input_filepath,
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echo_filepath=echo_path_filepath,
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render_filepath=render_input_filepath,
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capture_filepath=apm_input_filepath,
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apm_output_filepath=self._audioproc_wrapper.output_filepath,
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apm_reference_filepath=reference_signal_filepath)
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# Save simulation metadata.
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data_access.Metadata.SaveAudioTestDataPaths(
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output_path=evaluation_output_path,
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clean_capture_input_filepath=clean_capture_input_filepath,
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echo_free_capture_filepath=noisy_capture_input_filepath,
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echo_filepath=echo_path_filepath,
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render_filepath=render_input_filepath,
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capture_filepath=apm_input_filepath,
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apm_output_filepath=self._audioproc_wrapper.output_filepath,
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apm_reference_filepath=reference_signal_filepath)
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except exceptions.EvaluationScoreException as e:
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logging.warning('the evaluation failed: %s', e.message)
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continue
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def _SetTestInputSignalFilePaths(self, capture_input_filepaths,
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render_input_filepaths):
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@ -9,6 +9,7 @@
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"""Unit tests for the simulation module.
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"""
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import logging
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import os
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import shutil
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import sys
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@ -33,8 +34,9 @@ class TestApmModuleSimulator(unittest.TestCase):
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"""
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def setUp(self):
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"""Create temporary folder and fake audio track."""
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"""Create temporary folders and fake audio track."""
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self._output_path = tempfile.mkdtemp()
|
||||
self._tmp_path = tempfile.mkdtemp()
|
||||
|
||||
silence = pydub.AudioSegment.silent(duration=1000, frame_rate=48000)
|
||||
fake_signal = signal_processing.SignalProcessingUtils.GenerateWhiteNoise(
|
||||
@ -46,6 +48,7 @@ class TestApmModuleSimulator(unittest.TestCase):
|
||||
def tearDown(self):
|
||||
"""Recursively delete temporary folders."""
|
||||
shutil.rmtree(self._output_path)
|
||||
shutil.rmtree(self._tmp_path)
|
||||
|
||||
def testSimulation(self):
|
||||
# Instance dependencies to inject and mock.
|
||||
@ -87,3 +90,39 @@ class TestApmModuleSimulator(unittest.TestCase):
|
||||
min_number_of_simulations)
|
||||
self.assertGreaterEqual(len(evaluator.Run.call_args_list),
|
||||
min_number_of_simulations)
|
||||
|
||||
def testPureToneGenerationWithTotalHarmonicDistorsion(self):
|
||||
logging.warning = mock.MagicMock(name='warning')
|
||||
|
||||
# Instance simulator.
|
||||
simulator = simulation.ApmModuleSimulator(
|
||||
aechen_ir_database_path='',
|
||||
polqa_tool_bin_path=os.path.join(
|
||||
os.path.dirname(__file__), 'fake_polqa'),
|
||||
ap_wrapper=audioproc_wrapper.AudioProcWrapper(),
|
||||
evaluator=evaluation.ApmModuleEvaluator())
|
||||
|
||||
# What to simulate.
|
||||
config_files = ['apm_configs/default.json']
|
||||
input_files = [os.path.join(self._tmp_path, 'pure_tone-440_1000.wav')]
|
||||
eval_scores = ['thd']
|
||||
|
||||
# Should work.
|
||||
simulator.Run(
|
||||
config_filepaths=config_files,
|
||||
capture_input_filepaths=input_files,
|
||||
test_data_generator_names=['identity'],
|
||||
eval_score_names=eval_scores,
|
||||
output_dir=self._output_path)
|
||||
self.assertFalse(logging.warning.called)
|
||||
|
||||
# Warning expected.
|
||||
simulator.Run(
|
||||
config_filepaths=config_files,
|
||||
capture_input_filepaths=input_files,
|
||||
test_data_generator_names=['white_noise'], # Not allowed with THD.
|
||||
eval_score_names=eval_scores,
|
||||
output_dir=self._output_path)
|
||||
logging.warning.assert_called_with('the evaluation failed: %s', (
|
||||
'The THD score cannot be used with any test data generator other than '
|
||||
'"identity"'))
|
||||
|
||||
@ -147,11 +147,12 @@ class TestDataGenerator(object):
|
||||
raise exceptions.InputSignalCreatorException(
|
||||
'Cannot parse input signal file name')
|
||||
|
||||
signal = input_signal_creator.InputSignalCreator.Create(
|
||||
signal, metadata = input_signal_creator.InputSignalCreator.Create(
|
||||
filename_parts[0], filename_parts[1].split('_'))
|
||||
|
||||
signal_processing.SignalProcessingUtils.SaveWav(
|
||||
input_signal_filepath, signal)
|
||||
data_access.Metadata.SaveFileMetadata(input_signal_filepath, metadata)
|
||||
|
||||
def _Generate(
|
||||
self, input_signal_filepath, test_data_cache_path, base_output_path):
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user