[Feature Extractors] Fix kwargs to pre-trained

src/transformers/feature_extraction_utils.py

@@ -566,17 +566,17 @@ def from_dict(cls, feature_extractor_dict: Dict[str, Any], **kwargs) -> PreTrain
         """
         return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
 
-        feature_extractor = cls(**feature_extractor_dict)
-
         # Update feature_extractor with kwargs if needed
         to_remove = []
         for key, value in kwargs.items():
-            if hasattr(feature_extractor, key):
-                setattr(feature_extractor, key, value)
+            if key in feature_extractor_dict:
+                feature_extractor_dict[key] = value
                 to_remove.append(key)
         for key in to_remove:
             kwargs.pop(key, None)
 
+        feature_extractor = cls(**feature_extractor_dict)
+
         logger.info(f"Feature extractor {feature_extractor}")
         if return_unused_kwargs:
             return feature_extractor, kwargs

tests/models/whisper/test_feature_extraction_whisper.py

@@ -142,6 +142,20 @@ def test_feat_extract_to_json_file(self):
         self.assertTrue(np.allclose(mel_1, mel_2))
         self.assertEqual(dict_first, dict_second)
 
+    def test_feat_extract_from_pretrained_kwargs(self):
+        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
+            check_json_file_has_correct_format(saved_file)
+            feat_extract_second = self.feature_extraction_class.from_pretrained(
+                tmpdirname, feature_size=2 * self.feat_extract_dict["feature_size"]
+            )
+
+        mel_1 = feat_extract_first.mel_filters
+        mel_2 = feat_extract_second.mel_filters
+        self.assertTrue(2 * mel_1.shape[1] == mel_2.shape[1])
+
     def test_call(self):
         # Tests that all call wrap to encode_plus and batch_encode_plus
         feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())