[Whispe] Fix pipeline after timestamp merges (#21198)

* pass return_timestamps to pre-process

* add a test to test it

* test does not need device 0

* remove failing bit

* update test

src/transformers/pipelines/automatic_speech_recognition.py

@@ -400,6 +400,8 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
                     " only 1 version"
                 )
             forward_params["generate_kwargs"].update(generate_kwargs)
+        if return_timestamps is not None:
+            forward_params["generate_kwargs"]["return_timestamps"] = return_timestamps
 
         postprocess_params = {}
         if decoder_kwargs is not None:
@@ -523,6 +525,8 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
             generate_kwargs = {}
 
         is_last = model_inputs.pop("is_last")
+        return_timestamps = generate_kwargs.pop("return_timestamps", False)
+
         if self.type == "seq2seq":
             encoder = self.model.get_encoder()
             # Consume values so we can let extra information flow freely through
@@ -552,7 +556,7 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
             stride = model_inputs.pop("stride", None)
             tokens = self.model.generate(
                 input_features=model_inputs.pop("input_features"),
-                logits_processor=[WhisperTimeStampLogitsProcessor()],
+                logits_processor=[WhisperTimeStampLogitsProcessor()] if return_timestamps else None,
                 **generate_kwargs,
             )
             out = {"tokens": tokens}

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -291,6 +291,29 @@ 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_return_timestamps_in_preprocess(self):
+        pipe = pipeline(
+            task="automatic-speech-recognition",
+            model="openai/whisper-tiny",
+            chunk_length_s=8,
+            stride_length_s=1,
+        )
+        data = load_dataset("librispeech_asr", "clean", split="test", streaming=True)
+        sample = next(iter(data))
+        pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="en", task="transcribe")
+
+        res = pipe(sample["audio"]["array"])
+        self.assertEqual(res, {"text": " Conquered returned to its place amidst the tents."})
+        res = pipe(sample["audio"]["array"], return_timestamps=True)
+        self.assertEqual(
+            res,
+            {
+                "text": " Conquered returned to its place amidst the tents.",
+                "chunks": [{"text": " Conquered returned to its place amidst the tents.", "timestamp": (0.0, 3.36)}],
+            },
+        )
+
     @require_torch
     @slow
     def test_torch_whisper(self):