diff --git a/src/transformers/tokenization_utils.py b/src/transformers/tokenization_utils.py
index 6a5bff3679f8..df13a029a6c6 100644
--- a/src/transformers/tokenization_utils.py
+++ b/src/transformers/tokenization_utils.py
@@ -1077,7 +1077,7 @@ def convert_tokens_to_string(self, tokens: List[str]) -> str:
 
     def _decode(
         self,
-        token_ids: List[int],
+        token_ids: Union[int, List[int]],
         skip_special_tokens: bool = False,
         clean_up_tokenization_spaces: bool = None,
         spaces_between_special_tokens: bool = True,
@@ -1086,6 +1086,10 @@ def _decode(
         self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False)
 
         filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
+        # If given is a single id, prevents splitting the string in upcoming loop
+        if isinstance(filtered_tokens, str):
+            filtered_tokens = [filtered_tokens]
+
         legacy_added_tokens = set(self._added_tokens_encoder.keys()) - set(self.all_special_tokens) | {
             token for token in self.additional_special_tokens if self.convert_tokens_to_ids(token) >= self.vocab_size
         }
@@ -1096,7 +1100,7 @@ def _decode(
         current_sub_text = []
         # TODO @ArthurZ in version 5, special tokens should be handled in convert_tokens_to_string, while _convert_tokens_to_string
         for token in filtered_tokens:
-            if skip_special_tokens and token in self.all_special_ids:
+            if skip_special_tokens and token in self.all_special_tokens:
                 continue
             if token in legacy_added_tokens:
                 if current_sub_text:
diff --git a/tests/tokenization/test_tokenization_utils.py b/tests/tokenization/test_tokenization_utils.py
index b43923df84d7..2c8f71ba9772 100644
--- a/tests/tokenization/test_tokenization_utils.py
+++ b/tests/tokenization/test_tokenization_utils.py
@@ -253,6 +253,71 @@ def test_padding_accepts_tensors(self):
         self.assertTrue(isinstance(batch["input_ids"], np.ndarray))
         self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
 
+    @require_tokenizers
+    def test_decoding_single_token(self):
+        for tokenizer_class in [BertTokenizer, BertTokenizerFast]:
+            with self.subTest(f"{tokenizer_class}"):
+                tokenizer = tokenizer_class.from_pretrained("google-bert/bert-base-cased")
+
+                token_id = 2300
+                decoded_flat = tokenizer.decode(token_id)
+                decoded_list = tokenizer.decode([token_id])
+
+                self.assertEqual(decoded_flat, "Force")
+                self.assertEqual(decoded_list, "Force")
+
+                token_id = 0
+                decoded_flat = tokenizer.decode(token_id)
+                decoded_list = tokenizer.decode([token_id])
+
+                self.assertEqual(decoded_flat, "[PAD]")
+                self.assertEqual(decoded_list, "[PAD]")
+
+                last_item_id = tokenizer.vocab_size - 1
+                decoded_flat = tokenizer.decode(last_item_id)
+                decoded_list = tokenizer.decode([last_item_id])
+
+                self.assertEqual(decoded_flat, "##：")
+                self.assertEqual(decoded_list, "##：")
+
+    @require_tokenizers
+    def test_decoding_skip_special_tokens(self):
+        for tokenizer_class in [BertTokenizer, BertTokenizerFast]:
+            with self.subTest(f"{tokenizer_class}"):
+                tokenizer = tokenizer_class.from_pretrained("google-bert/bert-base-cased")
+                tokenizer.add_tokens(["ஐ"], special_tokens=True)
+
+                # test special token with other tokens, skip the special tokens
+                sentence = "This is a beautiful flower ஐ"
+                ids = tokenizer(sentence)["input_ids"]
+                decoded_sent = tokenizer.decode(ids, skip_special_tokens=True)
+                self.assertEqual(decoded_sent, "This is a beautiful flower")
+
+                # test special token with other tokens, do not skip the special tokens
+                ids = tokenizer(sentence)["input_ids"]
+                decoded_sent = tokenizer.decode(ids, skip_special_tokens=False)
+                self.assertEqual(decoded_sent, "[CLS] This is a beautiful flower ஐ [SEP]")
+
+                # test special token stand alone, skip the special tokens
+                sentence = "ஐ"
+                ids = tokenizer(sentence)["input_ids"]
+                decoded_sent = tokenizer.decode(ids, skip_special_tokens=True)
+                self.assertEqual(decoded_sent, "")
+
+                # test special token stand alone, do not skip the special tokens
+                ids = tokenizer(sentence)["input_ids"]
+                decoded_sent = tokenizer.decode(ids, skip_special_tokens=False)
+                self.assertEqual(decoded_sent, "[CLS] ஐ [SEP]")
+
+                # test single special token alone, skip
+                pad_id = 0
+                decoded_sent = tokenizer.decode(pad_id, skip_special_tokens=True)
+                self.assertEqual(decoded_sent, "")
+
+                # test single special token alone, do not skip
+                decoded_sent = tokenizer.decode(pad_id, skip_special_tokens=False)
+                self.assertEqual(decoded_sent, "[PAD]")
+
     @require_torch
     def test_padding_accepts_tensors_pt(self):
         import torch