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"""Contains the volume perturb augmentation model."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from data_utils.augmentor.base import AugmentorBase
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class ShiftPerturbAugmentor(AugmentorBase):
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"""Augmentation model for adding random shift perturbation.
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:param rng: Random generator object.
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:type rng: random.Random
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:param min_shift_ms: Minimal shift in milliseconds.
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:type min_shift_ms: float
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:param max_shift_ms: Maximal shift in milliseconds.
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:type max_shift_ms: float
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"""
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def __init__(self, rng, min_shift_ms, max_shift_ms):
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self._min_shift_ms = min_shift_ms
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self._max_shift_ms = max_shift_ms
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self._rng = rng
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def transform_audio(self, audio_segment):
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"""Shift audio.
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Note that this is an in-place transformation.
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:param audio_segment: Audio segment to add effects to.
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:type audio_segment: AudioSegmenet|SpeechSegment
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"""
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shift_ms = self._rng.uniform(self._min_shift_ms, self._max_shift_ms)
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audio_segment.shift(shift_ms)
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"""Test augmentor class."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import unittest
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from data_utils import audio
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from data_utils.augmentor.augmentation import AugmentationPipeline
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import random
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import numpy as np
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random_seed = 0
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#audio instance
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audio_data = [3.0517571e-05, -8.54492188e-04, -1.09863281e-03, -9.4604492e-04,\
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-1.31225586e-03, -1.09863281e-03, -1.73950195e-03, -2.1057189e-03,\
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-2.04467773e-03, -1.46484375e-03, -1.43432617e-03, -9.4604492e-04,\
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-1.95312500e-03, -1.86157227e-03, -2.10571289e-03, -2.3193354e-03,\
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-2.01416016e-03, -2.62451172e-03, -2.07519531e-03, -2.3803719e-03]
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audio_data = np.array(audio_data)
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samplerate = 10
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class TestAugmentor(unittest.TestCase):
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def test_volume(self):
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config_json = '[{"type": "volume","params": {"min_gain_dBFS": -15, '\
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'"max_gain_dBFS": 15},"prob": 1.0}]'
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aug_pipeline = AugmentationPipeline(
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augmentation_config=config_json, random_seed=random_seed)
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audio_seg = audio.AudioSegment(audio_data, samplerate)
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aug_pipeline.transform_audio(audio_seg)
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orig_audio = audio.AudioSegment(audio_data, samplerate)
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self.assertFalse(np.any(audio_seg.samples == orig_audio.samples))
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def test_speed(self):
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config_json = '[{"type":"speed","params": {"min_speed_rate": 0.9,' \
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'"max_speed_rate": 1.1},"prob": 1.0}]'
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aug_pipeline = AugmentationPipeline(
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augmentation_config=config_json, random_seed=random_seed)
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audio_seg = audio.AudioSegment(audio_data, samplerate)
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aug_pipeline.transform_audio(audio_seg)
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orig_audio = audio.AudioSegment(audio_data, samplerate)
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self.assertFalse(np.any(audio_seg.samples == orig_audio.samples))
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def test_resample(self):
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config_json = '[{"type":"resample","params": {"new_sample_rate":5},'\
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'"prob": 1.0}]'
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aug_pipeline = AugmentationPipeline(
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augmentation_config=config_json, random_seed=random_seed)
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audio_seg = audio.AudioSegment(audio_data, samplerate)
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aug_pipeline.transform_audio(audio_seg)
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self.assertTrue(audio_seg.sample_rate == 5)
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def test_bayesial(self):
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config_json = '[{"type":"bayesian_normal","params":{"target_db":-20,' \
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'"prior_db":-4, "prior_samples": -8, "startup_delay": 0.0},"prob":1.0}]'
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aug_pipeline = AugmentationPipeline(
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augmentation_config=config_json, random_seed=random_seed)
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audio_seg = audio.AudioSegment(audio_data, samplerate)
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aug_pipeline.transform_audio(audio_seg)
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orig_audio = audio.AudioSegment(audio_data, samplerate)
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self.assertFalse(np.any(audio_seg.samples == orig_audio.samples))
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if __name__ == '__main__':
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unittest.main()
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