[ASR Pipeline] Fix init with timestamps (#25438)

* [ASR Pipeline] Fix init

* refactor test

* change default kwarg setting

* only perform checks if we have to

* override init

* move pre/forward/post checks to sanitize

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -343,6 +343,58 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
         )
         # fmt: on
 
+    @require_torch
+    def test_return_timestamps_in_init(self):
+        # segment-level timestamps are accepted
+        model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
+        tokenizer = AutoTokenizer.from_pretrained("openai/whisper-tiny")
+        feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-tiny")
+
+        dummy_speech = np.ones(100)
+
+        pipe = pipeline(
+            task="automatic-speech-recognition",
+            model=model,
+            feature_extractor=feature_extractor,
+            tokenizer=tokenizer,
+            chunk_length_s=8,
+            stride_length_s=1,
+            return_timestamps=True,
+        )
+
+        _ = pipe(dummy_speech)
+
+        # word-level timestamps are accepted
+        pipe = pipeline(
+            task="automatic-speech-recognition",
+            model=model,
+            feature_extractor=feature_extractor,
+            tokenizer=tokenizer,
+            chunk_length_s=8,
+            stride_length_s=1,
+            return_timestamps="word",
+        )
+
+        _ = pipe(dummy_speech)
+
+        # char-level timestamps are not accepted
+        with self.assertRaisesRegex(
+            ValueError,
+            "^Whisper cannot return `char` timestamps, only word level or segment level timestamps. "
+            "Use `return_timestamps='word'` or `return_timestamps=True` respectively.$",
+        ):
+            pipe = pipeline(
+                task="automatic-speech-recognition",
+                model=model,
+                feature_extractor=feature_extractor,
+                tokenizer=tokenizer,
+                chunk_length_s=8,
+                stride_length_s=1,
+                return_timestamps="char",
+            )
+
+            _ = pipe(dummy_speech)
+
     @require_torch
     @slow
     def test_torch_whisper(self):