[Speech models] Disable non-existing chunking in tests (#15163)

tests/test_modeling_hubert.py

@@ -425,6 +425,10 @@ class HubertModelTest(ModelTesterMixin, unittest.TestCase):
         if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
             module.masked_spec_embed.data.fill_(3)
 
+    @unittest.skip(reason="Feed forward chunking is not implemented")
+    def test_feed_forward_chunking(self):
+        pass
+
     @slow
     def test_model_from_pretrained(self):
         model = HubertModel.from_pretrained("facebook/hubert-base-ls960")
@@ -572,6 +576,10 @@ class HubertRobustModelTest(ModelTesterMixin, unittest.TestCase):
         if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
             module.masked_spec_embed.data.fill_(3)
 
+    @unittest.skip(reason="Feed forward chunking is not implemented")
+    def test_feed_forward_chunking(self):
+        pass
+
     @slow
     def test_model_from_pretrained(self):
         model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft")

tests/test_modeling_sew.py

@@ -428,6 +428,10 @@ class SEWModelTest(ModelTesterMixin, unittest.TestCase):
         if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
             module.masked_spec_embed.data.fill_(3)
 
+    @unittest.skip(reason="Feed forward chunking is not implemented")
+    def test_feed_forward_chunking(self):
+        pass
+
     @slow
     def test_model_from_pretrained(self):
         model = SEWModel.from_pretrained("asapp/sew-tiny-100k")

tests/test_modeling_sew_d.py

@@ -441,6 +441,10 @@ class SEWDModelTest(ModelTesterMixin, unittest.TestCase):
         if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
             module.masked_spec_embed.data.fill_(3)
 
+    @unittest.skip(reason="Feed forward chunking is not implemented")
+    def test_feed_forward_chunking(self):
+        pass
+
     @slow
     def test_model_from_pretrained(self):
         model = SEWDModel.from_pretrained("asapp/sew-d-tiny-100k")

tests/test_modeling_unispeech.py

@@ -518,6 +518,10 @@ class UniSpeechRobustModelTest(ModelTesterMixin, unittest.TestCase):
 
         self.assertEqual(logits.shape, (1, 1498, 32))
 
+    @unittest.skip(reason="Feed forward chunking is not implemented")
+    def test_feed_forward_chunking(self):
+        pass
+
     @slow
     def test_model_from_pretrained(self):
         model = UniSpeechModel.from_pretrained("microsoft/unispeech-large-1500h-cv")

tests/test_modeling_unispeech_sat.py

@@ -541,6 +541,10 @@ class UniSpeechSatModelTest(ModelTesterMixin, unittest.TestCase):
 
         self.assertEqual(logits.shape, (4, 1498, 32))
 
+    @unittest.skip(reason="Feed forward chunking is not implemented")
+    def test_feed_forward_chunking(self):
+        pass
+
     @slow
     def test_model_from_pretrained(self):
         model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-base-plus")
@@ -776,6 +780,10 @@ class UniSpeechSatRobustModelTest(ModelTesterMixin, unittest.TestCase):
 
         self.assertEqual(logits.shape, (1, 1498, 32))
 
+    @unittest.skip(reason="Feed forward chunking is not implemented")
+    def test_feed_forward_chunking(self):
+        pass
+
     @slow
     def test_model_from_pretrained(self):
         model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-large")

tests/test_modeling_wav2vec2.py

@@ -605,6 +605,10 @@ class Wav2Vec2ModelTest(ModelTesterMixin, unittest.TestCase):
 
         self.assertEqual(logits.shape, (4, 1498, 32))
 
+    @unittest.skip(reason="Feed forward chunking is not implemented")
+    def test_feed_forward_chunking(self):
+        pass
+
     @slow
     def test_model_from_pretrained(self):
         model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
@@ -901,6 +905,10 @@ class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
 
         self.assertEqual(logits.shape, (1, 1498, 32))
 
+    @unittest.skip(reason="Feed forward chunking is not implemented")
+    def test_feed_forward_chunking(self):
+        pass
+
     @slow
     def test_model_from_pretrained(self):
         model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")