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131 lines
4.8 KiB
131 lines
4.8 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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def test_add_noise(tmpdir, device):
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paddle.device.set_device(device)
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from paddlespeech.vector.io.augment import AddNoise
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test_waveform = paddle.sin(
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paddle.arange(16000.0, dtype="float32")).unsqueeze(0)
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test_noise = paddle.cos(
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paddle.arange(16000.0, dtype="float32")).unsqueeze(0)
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wav_lens = paddle.ones([1], dtype="float32")
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# Edge cases
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no_noise = AddNoise(mix_prob=0.0)
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assert no_noise(test_waveform, wav_lens).allclose(test_waveform)
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def test_speed_perturb(device):
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paddle.device.set_device(device)
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from paddlespeech.vector.io.augment import SpeedPerturb
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test_waveform = paddle.sin(
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paddle.arange(16000.0, dtype="float32")).unsqueeze(0)
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# Edge cases
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no_perturb = SpeedPerturb(16000, perturb_prob=0.0)
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assert no_perturb(test_waveform).allclose(test_waveform)
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no_perturb = SpeedPerturb(16000, speeds=[100])
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assert no_perturb(test_waveform).allclose(test_waveform)
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# # Half speed
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half_speed = SpeedPerturb(16000, speeds=[50])
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assert half_speed(test_waveform).allclose(test_waveform[:, ::2], atol=3e-1)
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def test_babble(device):
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paddle.device.set_device(device)
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from paddlespeech.vector.io.augment import AddBabble
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test_waveform = paddle.stack(
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(paddle.sin(paddle.arange(16000.0, dtype="float32")),
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paddle.cos(paddle.arange(16000.0, dtype="float32")), ))
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lengths = paddle.ones([2])
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# Edge cases
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no_babble = AddBabble(mix_prob=0.0)
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assert no_babble(test_waveform, lengths).allclose(test_waveform)
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no_babble = AddBabble(speaker_count=1, snr_low=1000, snr_high=1000)
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assert no_babble(test_waveform, lengths).allclose(test_waveform)
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# One babbler just averages the two speakers
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babble = AddBabble(speaker_count=1).to(device)
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expected = (test_waveform + test_waveform.roll(1, 0)) / 2
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assert babble(test_waveform, lengths).allclose(expected, atol=1e-4)
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def test_drop_freq(device):
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paddle.device.set_device(device)
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from paddlespeech.vector.io.augment import DropFreq
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test_waveform = paddle.sin(
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paddle.arange(16000.0, dtype="float32")).unsqueeze(0)
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# Edge cases
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no_drop = DropFreq(drop_prob=0.0)
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assert no_drop(test_waveform).allclose(test_waveform)
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no_drop = DropFreq(drop_count_low=0, drop_count_high=0)
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assert no_drop(test_waveform).allclose(test_waveform)
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# Check case where frequency range *does not* include signal frequency
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drop_diff_freq = DropFreq(drop_freq_low=0.5, drop_freq_high=0.9)
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assert drop_diff_freq(test_waveform).allclose(test_waveform, atol=1e-1)
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# Check case where frequency range *does* include signal frequency
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drop_same_freq = DropFreq(drop_freq_low=0.28, drop_freq_high=0.28)
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assert drop_same_freq(test_waveform).allclose(
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paddle.zeros([1, 16000]), atol=4e-1)
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def test_drop_chunk(device):
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paddle.device.set_device(device)
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from paddlespeech.vector.io.augment import DropChunk
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test_waveform = paddle.sin(
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paddle.arange(16000.0, dtype="float32")).unsqueeze(0)
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lengths = paddle.ones([1])
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# Edge cases
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no_drop = DropChunk(drop_prob=0.0)
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assert no_drop(test_waveform, lengths).allclose(test_waveform)
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no_drop = DropChunk(drop_length_low=0, drop_length_high=0)
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assert no_drop(test_waveform, lengths).allclose(test_waveform)
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no_drop = DropChunk(drop_count_low=0, drop_count_high=0)
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assert no_drop(test_waveform, lengths).allclose(test_waveform)
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no_drop = DropChunk(drop_start=0, drop_end=0)
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assert no_drop(test_waveform, lengths).allclose(test_waveform)
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# Specify all parameters to ensure it is deterministic
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dropper = DropChunk(
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drop_length_low=100,
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drop_length_high=100,
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drop_count_low=1,
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drop_count_high=1,
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drop_start=100,
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drop_end=200,
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noise_factor=0.0, )
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expected_waveform = test_waveform.clone()
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expected_waveform[:, 100:200] = 0.0
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assert dropper(test_waveform, lengths).allclose(expected_waveform)
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# Make sure amplitude is similar before and after
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dropper = DropChunk(noise_factor=1.0)
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drop_amplitude = dropper(test_waveform, lengths).abs().mean()
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orig_amplitude = test_waveform.abs().mean()
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assert drop_amplitude.allclose(orig_amplitude, atol=1e-2)
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