MFCC test refactor (#1618)

test/torchaudio_unittest/transforms/transforms_test.py

@@ -127,41 +127,58 @@ class Tester(common_utils.TorchaudioTestCase):
         self.assertTrue(fb_matrix_transform.fb.sum(1).ge(0.).all())
         self.assertEqual(fb_matrix_transform.fb.size(), (400, 100))
 
-    def test_mfcc(self):
-        audio_orig = self.waveform.clone()
-        audio_scaled = self.scale(audio_orig)  # (1, 16000)
-
+    def test_mfcc_defaults(self):
+        """Check the default configuration of the MFCC transform.
+        """
         sample_rate = 16000
+        audio = common_utils.get_whitenoise(sample_rate=sample_rate)
+
         n_mfcc = 40
-        n_mels = 128
         mfcc_transform = torchaudio.transforms.MFCC(sample_rate=sample_rate,
                                                     n_mfcc=n_mfcc,
                                                     norm='ortho')
-        # check defaults
-        torch_mfcc = mfcc_transform(audio_scaled)  # (1, 40, 321)
-        self.assertTrue(torch_mfcc.dim() == 3)
-        self.assertTrue(torch_mfcc.shape[1] == n_mfcc)
-        self.assertTrue(torch_mfcc.shape[2] == 321)
-        # check melkwargs are passed through
+        torch_mfcc = mfcc_transform(audio)  # (1, 40, 81)
+        self.assertEqual(torch_mfcc.dim(), 3)
+        self.assertEqual(torch_mfcc.shape[1], n_mfcc)
+        self.assertEqual(torch_mfcc.shape[2], 81)
+
+    def test_mfcc_kwargs_passthrough(self):
+        """Check kwargs get correctly passed to the MelSpectrogram transform.
+        """
+        sample_rate = 16000
+        audio = common_utils.get_whitenoise(sample_rate=sample_rate)
+
+        n_mfcc = 40
         melkwargs = {'win_length': 200}
-        mfcc_transform2 = torchaudio.transforms.MFCC(sample_rate=sample_rate,
-                                                     n_mfcc=n_mfcc,
-                                                     norm='ortho',
-                                                     melkwargs=melkwargs)
-        torch_mfcc2 = mfcc_transform2(audio_scaled)  # (1, 40, 641)
-        self.assertTrue(torch_mfcc2.shape[2] == 641)
+        mfcc_transform = torchaudio.transforms.MFCC(sample_rate=sample_rate,
+                                                    n_mfcc=n_mfcc,
+                                                    norm='ortho',
+                                                    melkwargs=melkwargs)
+        torch_mfcc = mfcc_transform(audio)  # (1, 40, 161)
+        self.assertEqual(torch_mfcc.shape[2], 161)
+
+    def test_mfcc_norms(self):
+        """Check if MFCC-DCT norms work correctly.
+        """
+        sample_rate = 16000
+        audio = common_utils.get_whitenoise(sample_rate=sample_rate)
 
+        n_mfcc = 40
+        n_mels = 128
+        mfcc_transform = torchaudio.transforms.MFCC(sample_rate=sample_rate,
+                                                    n_mfcc=n_mfcc,
+                                                    norm='ortho')
         # check norms work correctly
         mfcc_transform_norm_none = torchaudio.transforms.MFCC(sample_rate=sample_rate,
                                                               n_mfcc=n_mfcc,
                                                               norm=None)
-        torch_mfcc_norm_none = mfcc_transform_norm_none(audio_scaled)  # (1, 40, 321)
+        torch_mfcc_norm_none = mfcc_transform_norm_none(audio)  # (1, 40, 81)
 
-        norm_check = torch_mfcc.clone()
+        norm_check = mfcc_transform(audio)
         norm_check[:, 0, :] *= math.sqrt(n_mels) * 2
         norm_check[:, 1:, :] *= math.sqrt(n_mels / 2) * 2
 
-        self.assertTrue(torch_mfcc_norm_none.allclose(norm_check))
+        self.assertEqual(torch_mfcc_norm_none, norm_check)
 
     def test_resample_size(self):
         input_path = common_utils.get_asset_path('sinewave.wav')