[Wav2Vec2ForPretraining] Correct checkpoints wav2vec2 & fix tests (#12089)

* fix_torch_device_generate_test

* remove @

* fix tests

tests/test_modeling_wav2vec2.py

@@ -349,6 +349,8 @@ class Wav2Vec2ModelTest(ModelTesterMixin, unittest.TestCase):
             module.bias.data.fill_(3)
         if hasattr(module, "codevectors") and module.codevectors is not None:
             module.codevectors.data.fill_(3)
+        if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
+            module.masked_spec_embed.data.fill_(3)
 
     @slow
     def test_model_from_pretrained(self):
@@ -487,6 +489,8 @@ class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
             module.bias.data.fill_(3)
         if hasattr(module, "codevectors") and module.codevectors is not None:
             module.codevectors.data.fill_(3)
+        if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
+            module.masked_spec_embed.data.fill_(3)
 
     def test_model_for_pretraining(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@@ -677,10 +681,10 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
         self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
 
     def test_inference_integration(self):
-        model = Wav2Vec2ForPreTraining.from_pretrained("patrickvonplaten/wav2vec2-base")
+        model = Wav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-base")
         model.to(torch_device)
         feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
-            "patrickvonplaten/wav2vec2-base", return_attention_mask=True
+            "facebook/wav2vec2-base", return_attention_mask=True
         )
         input_speech = self._load_datasamples(2)
 
@@ -723,10 +727,10 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
         self.assertTrue(torch.allclose(cosine_sim_masked, expected_cosine_sim_masked, atol=1e-3))
 
     def test_inference_pretrained(self):
-        model = Wav2Vec2ForPreTraining.from_pretrained("patrickvonplaten/wav2vec2-base")
+        model = Wav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-base")
         model.to(torch_device)
         feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
-            "patrickvonplaten/wav2vec2-base", return_attention_mask=True
+            "facebook/wav2vec2-base", return_attention_mask=True
         )
         input_speech = self._load_datasamples(2)
 
@@ -761,7 +765,7 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
 
         # ... now compare to randomly initialized model
 
-        config = Wav2Vec2Config.from_pretrained("patrickvonplaten/wav2vec2-base")
+        config = Wav2Vec2Config.from_pretrained("facebook/wav2vec2-base")
         model_rand = Wav2Vec2ForPreTraining(config).to(torch_device).eval()
 
         with torch.no_grad():
@@ -785,9 +789,10 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
         # => the cosine similarity between quantized states and predicted states is very likely < 0.1
         self.assertTrue(cosine_sim_masked.mean().item() - 5 * cosine_sim_masked_rand.mean().item() > 0)
 
+    @unittest.skipIf(torch_device != "cpu", "cannot make deterministic on GPU")
     def test_loss_pretraining(self):
         model = Wav2Vec2ForPreTraining.from_pretrained(
-            "patrickvonplaten/wav2vec2-base",
+            "facebook/wav2vec2-base",
             attention_dropout=0.0,
             feat_proj_dropout=0.0,
             hidden_dropout=0.0,
@@ -796,7 +801,7 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
         model.to(torch_device).train()
 
         feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
-            "patrickvonplaten/wav2vec2-base", return_attention_mask=True
+            "facebook/wav2vec2-base", return_attention_mask=True
         )
         input_speech = self._load_datasamples(2)
 
@@ -829,6 +834,6 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
         self.assertTrue(abs(diversity_loss.item() - 0.8859) < 1e-3)
 
         # check overall loss (contrastive loss + diversity loss)
-        expected_loss = 62.5170 if model.device.type == "cpu" else 50.3612
+        expected_loss = 62.5170
 
         self.assertTrue(abs(outputs.loss.item() - expected_loss) < 1e-3)