diff --git a/src/transformers/generation/streamers.py b/src/transformers/generation/streamers.py
index 719c067d2..4006256f8 100644
--- a/src/transformers/generation/streamers.py
+++ b/src/transformers/generation/streamers.py
@@ -14,7 +14,7 @@
 # limitations under the License.
 
 from queue import Queue
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Optional
 
 
 if TYPE_CHECKING:
@@ -133,6 +133,9 @@ class TextIteratorStreamer(TextStreamer):
             The tokenized used to decode the tokens.
         skip_prompt (`bool`, *optional*, defaults to `False`):
             Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
+        timeout (`float`, *optional*):
+            The timeout for the text queue. If `None`, the queue will block indefinitely. Useful to handle exceptions
+            in `.generate()`, when it is called in a separate thread.
         decode_kwargs (`dict`, *optional*):
             Additional keyword arguments to pass to the tokenizer's `decode` method.
 
@@ -159,22 +162,25 @@ class TextIteratorStreamer(TextStreamer):
         ```
     """
 
-    def __init__(self, tokenizer: "AutoTokenizer", skip_prompt: bool = False, **decode_kwargs):
+    def __init__(
+        self, tokenizer: "AutoTokenizer", skip_prompt: bool = False, timeout: Optional[float] = None, **decode_kwargs
+    ):
         super().__init__(tokenizer, skip_prompt, **decode_kwargs)
         self.text_queue = Queue()
         self.stop_signal = None
+        self.timeout = timeout
 
     def on_finalized_text(self, text: str, stream_end: bool = False):
         """Put the new text in the queue. If the stream is ending, also put a stop signal in the queue."""
-        self.text_queue.put(text)
+        self.text_queue.put(text, timeout=self.timeout)
         if stream_end:
-            self.text_queue.put(self.stop_signal)
+            self.text_queue.put(self.stop_signal, timeout=self.timeout)
 
     def __iter__(self):
         return self
 
     def __next__(self):
-        value = self.text_queue.get()
+        value = self.text_queue.get(timeout=self.timeout)
         if value == self.stop_signal:
             raise StopIteration()
         else:
diff --git a/tests/generation/test_streamers.py b/tests/generation/test_streamers.py
index 7214e56cd..361f39e03 100644
--- a/tests/generation/test_streamers.py
+++ b/tests/generation/test_streamers.py
@@ -14,6 +14,7 @@
 # limitations under the License.
 
 import unittest
+from queue import Empty
 from threading import Thread
 
 from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
@@ -102,3 +103,20 @@ class StreamerTester(unittest.TestCase):
         streamer_text = cs.out[:-1]  # Remove the final "\n"
         streamer_text_tokenized = tokenizer(streamer_text, return_tensors="pt")
         self.assertEqual(streamer_text_tokenized.input_ids.shape, (1, 1))
+
+    def test_iterator_streamer_timeout(self):
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
+        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
+        model.config.eos_token_id = -1
+
+        input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
+        streamer = TextIteratorStreamer(tokenizer, timeout=0.001)
+        generation_kwargs = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
+        thread = Thread(target=model.generate, kwargs=generation_kwargs)
+        thread.start()
+
+        # The streamer will timeout after 0.001 seconds, so an exception will be raised
+        with self.assertRaises(Empty):
+            streamer_text = ""
+            for new_text in streamer:
+                streamer_text += new_text