is_ctc needs to be updated to `self.type == "ctc". (#15194)

* `is_ctc` needs to be updated to `self.type == "ctc".

* Adding fast test for this functionality.

src/transformers/pipelines/automatic_speech_recognition.py

@@ -215,7 +215,7 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
             stride_left = int(round(stride_length_s[0] * self.feature_extractor.sampling_rate))
             stride_right = int(round(stride_length_s[1] * self.feature_extractor.sampling_rate))
 
-            if not self.is_ctc:
+            if self.type != "ctc":
                 raise ValueError(
                     "`chunk_length_s` is only valid for CTC models, use other chunking options for other models"
                 )

tests/test_pipelines_automatic_speech_recognition.py

@@ -278,6 +278,23 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase, metaclass=Pipel
         output = speech_recognizer(filename)
         self.assertEqual(output, {"text": "a man said to the universe sir i exist"})
 
+    @require_torch
+    def test_chunking_fast(self):
+        speech_recognizer = pipeline(
+            task="automatic-speech-recognition",
+            model="hf-internal-testing/tiny-random-wav2vec2",
+            chunk_length_s=10.0,
+        )
+
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
+        audio = ds[40]["audio"]["array"]
+
+        n_repeats = 2
+        audio_tiled = np.tile(audio, n_repeats)
+        output = speech_recognizer([audio_tiled], batch_size=2)
+        self.assertEqual(output, [{"text": ANY(str)}])
+        self.assertEqual(output[0]["text"][:6], "ZBT ZC")
+
     @require_torch
     @slow
     def test_chunking(self):