Refactor Kaldi compatibility tests (#1359)

* Refactor Kaldi compatibility tests
Co-authored-by: Jeff Hwang <jeffhwang@fb.com>

test/torchaudio_unittest/README.md

@@ -54,7 +54,7 @@ The following is an overview of the tests and related modules for `torchaudio`.
     Test suite for numerical compatibility against librosa.
 - [SoX compatibility test](./transforms/sox_compatibility_test.py)
     Test suite for numerical compatibility against SoX.
-- [Kaldi compatibility test](./kaldi_compatibility_test.py)
+- [Kaldi compatibility test](./transforms/kaldi_compatibility_impl.py)
     Test suite for numerical compatibility against Kaldi.
 
 #### Result consistency with PyTorch framework

test/torchaudio_unittest/functional/kaldi_compatibility_cpu_test.py

 import torch
 
 from torchaudio_unittest.common_utils import PytorchTestCase
-from .kaldi_compatibility_test_impl import KaldiCPUOnly
+from .kaldi_compatibility_test_impl import Kaldi, KaldiCPUOnly
 
 
 class TestKaldiCPUOnly(KaldiCPUOnly, PytorchTestCase):
     dtype = torch.float32
     device = torch.device('cpu')
+
+
+class TestKaldiFloat32(Kaldi, PytorchTestCase):
+    dtype = torch.float32
+    device = torch.device('cpu')
+
+
+class TestKaldiFloat64(Kaldi, PytorchTestCase):
+    dtype = torch.float64
+    device = torch.device('cpu')

test/torchaudio_unittest/functional/kaldi_compatibility_cuda_test.py

+import torch
+
+from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
+from .kaldi_compatibility_test_impl import Kaldi
+
+
+@skipIfNoCuda
+class TestKaldiFloat32(Kaldi, PytorchTestCase):
+    dtype = torch.float32
+    device = torch.device('cuda')
+
+
+@skipIfNoCuda
+class TestKaldiFloat64(Kaldi, PytorchTestCase):
+    dtype = torch.float64
+    device = torch.device('cuda')

test/torchaudio_unittest/functional/kaldi_compatibility_test_impl.py

 from parameterized import parameterized
+import torch
 import torchaudio.functional as F
 
 from torchaudio_unittest.common_utils import (
@@ -15,6 +16,28 @@ from torchaudio_unittest.common_utils.kaldi_utils import (
 )
 
 
+class Kaldi(TempDirMixin, TestBaseMixin):
+    def assert_equal(self, output, *, expected, rtol=None, atol=None):
+        expected = expected.to(dtype=self.dtype, device=self.device)
+        self.assertEqual(output, expected, rtol=rtol, atol=atol)
+
+    @skipIfNoExec('apply-cmvn-sliding')
+    def test_sliding_window_cmn(self):
+        """sliding_window_cmn should be numerically compatible with apply-cmvn-sliding"""
+        kwargs = {
+            'cmn_window': 600,
+            'min_cmn_window': 100,
+            'center': False,
+            'norm_vars': False,
+        }
+
+        tensor = torch.randn(40, 10, dtype=self.dtype, device=self.device)
+        result = F.sliding_window_cmn(tensor, **kwargs)
+        command = ['apply-cmvn-sliding'] + convert_args(**kwargs) + ['ark:-', 'ark:-']
+        kaldi_result = run_kaldi(command, 'ark', tensor)
+        self.assert_equal(result, expected=kaldi_result)
+
+
 class KaldiCPUOnly(TempDirMixin, TestBaseMixin):
     def assert_equal(self, output, *, expected, rtol=None, atol=None):
         expected = expected.to(dtype=self.dtype, device=self.device)

test/torchaudio_unittest/kaldi_compatibility_impl.py → test/torchaudio_unittest/transforms/kaldi_compatibility_impl.py

 """Test suites for checking numerical compatibility against Kaldi"""
-import torch
-import torchaudio.functional as F
 import torchaudio.compliance.kaldi
 from parameterized import parameterized
 
@@ -23,22 +21,6 @@ class Kaldi(TempDirMixin, TestBaseMixin):
         expected = expected.to(dtype=self.dtype, device=self.device)
         self.assertEqual(output, expected, rtol=rtol, atol=atol)
 
-    @skipIfNoExec('apply-cmvn-sliding')
-    def test_sliding_window_cmn(self):
-        """sliding_window_cmn should be numerically compatible with apply-cmvn-sliding"""
-        kwargs = {
-            'cmn_window': 600,
-            'min_cmn_window': 100,
-            'center': False,
-            'norm_vars': False,
-        }
-
-        tensor = torch.randn(40, 10, dtype=self.dtype, device=self.device)
-        result = F.sliding_window_cmn(tensor, **kwargs)
-        command = ['apply-cmvn-sliding'] + convert_args(**kwargs) + ['ark:-', 'ark:-']
-        kaldi_result = run_kaldi(command, 'ark', tensor)
-        self.assert_equal(result, expected=kaldi_result)
-
     @parameterized.expand(load_params('kaldi_test_fbank_args.json'))
     @skipIfNoExec('compute-fbank-feats')
     def test_fbank(self, kwargs):