allow unused input parameters passthrough when chunking in asr pipeli…

…nes (#33889)

* allow unused parameter passthrough when chunking in asr pipelines

* format code

* format

* run fixup

* update tests

* update parameters to pipline in test

* updates parametrs in tests

* change spelling in gitignore

* revert .gitignore to main

* add git ignore of devcontainer folder

* assert asr output follows expected inference output type

* run fixup

* Remove .devcontainer from .gitignore

* remove compliance check

src/transformers/pipelines/automatic_speech_recognition.py

@@ -434,7 +434,7 @@ def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None):
             for item in chunk_iter(
                 inputs, self.feature_extractor, chunk_len, stride_left, stride_right, self.torch_dtype
             ):
-                yield item
+                yield {**item, **extra}
         else:
             if self.type == "seq2seq_whisper" and inputs.shape[0] > self.feature_extractor.n_samples:
                 processed = self.feature_extractor(

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -1443,6 +1443,25 @@ def test_chunking_fast(self):
         self.assertEqual(output, [{"text": ANY(str)}])
         self.assertEqual(output[0]["text"][:6], "ZBT ZC")
 
+    @require_torch
+    def test_input_parameter_passthrough(self):
+        """Test that chunked vs non chunked versions of ASR pipelines returns the same structure for the same inputs."""
+        speech_recognizer = pipeline(
+            task="automatic-speech-recognition",
+            model="hf-internal-testing/tiny-random-wav2vec2",
+        )
+
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
+        audio = ds[40]["audio"]["array"]
+
+        inputs = {"raw": audio, "sampling_rate": 16_000, "id": 1}
+
+        chunked_output = speech_recognizer(inputs.copy(), chunk_length_s=30)
+        non_chunked_output = speech_recognizer(inputs.copy())
+        assert (
+            chunked_output.keys() == non_chunked_output.keys()
+        ), "The output structure should be the same for chunked vs non-chunked versions of asr pipelines."
+
     @require_torch
     def test_return_timestamps_ctc_fast(self):
         speech_recognizer = pipeline(