add voxceleb augment unit test, test=doc

paddlespeech/vector/io/augment.py

@@ -178,7 +178,8 @@ class DropChunk(nn.Layer):
             # Update waveform
             if not self.noise_factor:
                 for j in range(drop_times[i]):
-                    dropped_waveform[i, start[j]:end[j]] = 0.0
+                    if start[j] < end[j]:
+                        dropped_waveform[i, start[j]:end[j]] = 0.0
             else:
                 # Uniform distribution of -2 to +2 * avg amplitude should
                 # preserve the average for normalization

tests/unit/vector/conftest.py

@@ -0,0 +1,11 @@
+def pytest_addoption(parser):
+    parser.addoption("--device", action="store", default="cpu")
+
+
+def pytest_generate_tests(metafunc):
+    # This is called for every test. Only get/set command line arguments
+    # if the argument is specified in the list of test "fixturenames".
+    option_value = metafunc.config.option.device
+    if "device" in metafunc.fixturenames and option_value is not None:
+        metafunc.parametrize("device", [option_value])
+

tests/unit/vector/test_augment.py

@@ -0,0 +1,138 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+
+import numpy as np
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle.io import BatchSampler
+from paddle.io import DataLoader
+from paddle.io import Dataset
+
+
+def test_add_noise(tmpdir, device):
+    paddle.device.set_device(device)
+    from paddlespeech.vector.io.augment import AddNoise
+
+    test_waveform = paddle.sin(
+        paddle.arange(16000.0, dtype="float32")).unsqueeze(0)
+    test_noise = paddle.cos(
+        paddle.arange(16000.0, dtype="float32")).unsqueeze(0)
+    wav_lens = paddle.ones([1], dtype="float32")
+
+    # Edge cases
+    no_noise = AddNoise(mix_prob=0.0)
+    assert no_noise(test_waveform, wav_lens).allclose(test_waveform)
+
+
+def test_speed_perturb(device):
+    paddle.device.set_device(device)
+    from paddlespeech.vector.io.augment import SpeedPerturb
+
+    test_waveform = paddle.sin(
+        paddle.arange(16000.0, dtype="float32")).unsqueeze(0)
+
+    # Edge cases
+    no_perturb = SpeedPerturb(16000, perturb_prob=0.0)
+    assert no_perturb(test_waveform).allclose(test_waveform)
+    no_perturb = SpeedPerturb(16000, speeds=[100])
+    assert no_perturb(test_waveform).allclose(test_waveform)
+
+    # # Half speed
+    half_speed = SpeedPerturb(16000, speeds=[50])
+    assert half_speed(test_waveform).allclose(test_waveform[:, ::2], atol=3e-1)
+
+
+def test_babble(device):
+    paddle.device.set_device(device)
+    from paddlespeech.vector.io.augment import AddBabble
+
+    test_waveform = paddle.stack(
+        (paddle.sin(paddle.arange(16000.0, dtype="float32")),
+         paddle.cos(paddle.arange(16000.0, dtype="float32")), ))
+    lengths = paddle.ones([2])
+
+    # Edge cases
+    no_babble = AddBabble(mix_prob=0.0)
+    assert no_babble(test_waveform, lengths).allclose(test_waveform)
+    no_babble = AddBabble(speaker_count=1, snr_low=1000, snr_high=1000)
+    assert no_babble(test_waveform, lengths).allclose(test_waveform)
+
+    # One babbler just averages the two speakers
+    babble = AddBabble(speaker_count=1).to(device)
+    expected = (test_waveform + test_waveform.roll(1, 0)) / 2
+    assert babble(test_waveform, lengths).allclose(expected, atol=1e-4)
+
+
+def test_drop_freq(device):
+    paddle.device.set_device(device)
+    from paddlespeech.vector.io.augment import DropFreq
+
+    test_waveform = paddle.sin(
+        paddle.arange(16000.0, dtype="float32")).unsqueeze(0)
+
+    # Edge cases
+    no_drop = DropFreq(drop_prob=0.0)
+    assert no_drop(test_waveform).allclose(test_waveform)
+    no_drop = DropFreq(drop_count_low=0, drop_count_high=0)
+    assert no_drop(test_waveform).allclose(test_waveform)
+
+    # Check case where frequency range *does not* include signal frequency
+    drop_diff_freq = DropFreq(drop_freq_low=0.5, drop_freq_high=0.9)
+    assert drop_diff_freq(test_waveform).allclose(test_waveform, atol=1e-1)
+
+    # Check case where frequency range *does* include signal frequency
+    drop_same_freq = DropFreq(drop_freq_low=0.28, drop_freq_high=0.28)
+    assert drop_same_freq(test_waveform).allclose(
+        paddle.zeros([1, 16000]), atol=4e-1)
+
+
+def test_drop_chunk(device):
+    paddle.device.set_device(device)
+    from paddlespeech.vector.io.augment import DropChunk
+
+    test_waveform = paddle.sin(
+        paddle.arange(16000.0, dtype="float32")).unsqueeze(0)
+    lengths = paddle.ones([1])
+
+    # Edge cases
+    no_drop = DropChunk(drop_prob=0.0)
+    assert no_drop(test_waveform, lengths).allclose(test_waveform)
+    no_drop = DropChunk(drop_length_low=0, drop_length_high=0)
+    assert no_drop(test_waveform, lengths).allclose(test_waveform)
+    no_drop = DropChunk(drop_count_low=0, drop_count_high=0)
+    assert no_drop(test_waveform, lengths).allclose(test_waveform)
+    no_drop = DropChunk(drop_start=0, drop_end=0)
+    assert no_drop(test_waveform, lengths).allclose(test_waveform)
+
+    # Specify all parameters to ensure it is deterministic
+    dropper = DropChunk(
+        drop_length_low=100,
+        drop_length_high=100,
+        drop_count_low=1,
+        drop_count_high=1,
+        drop_start=100,
+        drop_end=200,
+        noise_factor=0.0, )
+    expected_waveform = test_waveform.clone()
+    expected_waveform[:, 100:200] = 0.0
+
+    assert dropper(test_waveform, lengths).allclose(expected_waveform)
+
+    # Make sure amplitude is similar before and after
+    dropper = DropChunk(noise_factor=1.0)
+    drop_amplitude = dropper(test_waveform, lengths).abs().mean()
+    orig_amplitude = test_waveform.abs().mean()
+    assert drop_amplitude.allclose(orig_amplitude, atol=1e-2)