Add test for InverseMelScale

test/torchaudio_unittest/transforms/transforms_cpu_test.py

@@ -0,0 +1,14 @@
+import torch
+
+from torchaudio_unittest.common_utils import PytorchTestCase
+from . transforms_test_impl import TransformsTestBase
+
+
+class TransformsCPUFloat32Test(TransformsTestBase, PytorchTestCase):
+    device = 'cpu'
+    dtype = torch.float32
+
+
+class TransformsCPUFloat64Test(TransformsTestBase, PytorchTestCase):
+    device = 'cpu'
+    dtype = torch.float64

test/torchaudio_unittest/transforms/transforms_cuda_test.py

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+import torch
+
+from torchaudio_unittest.common_utils import (
+    PytorchTestCase,
+    skipIfNoCuda,
+)
+from . transforms_test_impl import TransformsTestBase
+
+
+@skipIfNoCuda
+class TransformsCUDAFloat32Test(TransformsTestBase, PytorchTestCase):
+    device = 'cuda'
+    dtype = torch.float32
+
+
+@skipIfNoCuda
+class TransformsCUDAFloat64Test(TransformsTestBase, PytorchTestCase):
+    device = 'cuda'
+    dtype = torch.float64

test/torchaudio_unittest/transforms/transforms_test_impl.py

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+import torch
+import torchaudio.transforms as T
+
+from torchaudio_unittest.common_utils import (
+    TestBaseMixin,
+    get_whitenoise,
+    get_spectrogram,
+)
+
+
+def _get_ratio(mat):
+    return (mat.sum() / mat.numel()).item()
+
+
+class TransformsTestBase(TestBaseMixin):
+    def test_InverseMelScale(self):
+        """Gauge the quality of InverseMelScale transform.
+
+        As InverseMelScale is currently implemented with
+        random initialization + iterative optimization,
+        it is not practically possible to assert the difference between
+        the estimated spectrogram and the original spectrogram as a whole.
+        Estimated spectrogram has very huge descrepency locally.
+        Thus in this test we gauge what percentage of elements are bellow
+        certain tolerance.
+        At the moment, the quality of estimated spectrogram is not good.
+        When implementation is changed in a way it makes the quality even worse,
+        this test will fail.
+        """
+        n_fft = 400
+        power = 1
+        n_mels = 64
+        sample_rate = 8000
+
+        n_stft = n_fft // 2 + 1
+
+        # Generate reference spectrogram and input mel-scaled spectrogram
+        expected = get_spectrogram(
+            get_whitenoise(sample_rate=sample_rate, duration=1, n_channels=2),
+            n_fft=n_fft, power=power).to(self.device, self.dtype)
+        input = T.MelScale(
+            n_mels=n_mels, sample_rate=sample_rate
+        ).to(self.device, self.dtype)(expected)
+
+        # Run transform
+        transform = T.InverseMelScale(
+            n_stft, n_mels=n_mels, sample_rate=sample_rate).to(self.device, self.dtype)
+        torch.random.manual_seed(0)
+        result = transform(input)
+
+        # Compare
+        epsilon = 1e-60
+        relative_diff = torch.abs((result - expected) / (expected + epsilon))
+
+        for tol in [1e-1, 1e-3, 1e-5, 1e-10]:
+            print(
+                f"Ratio of relative diff smaller than {tol:e} is "
+                f"{_get_ratio(relative_diff < tol)}")
+        assert _get_ratio(relative_diff < 1e-1) > 0.2
+        assert _get_ratio(relative_diff < 1e-3) > 5e-3
+        assert _get_ratio(relative_diff < 1e-5) > 1e-5