cc: @ylacombe here

Hey @Rocketknight1, thanks for opening the PR!
In theory, I don't see any problems with this fix, have you been able to run every slow ASR pipeline tests here ?

@ylacombe there are some slow tests that I can't get working on my local machine (even on main). However, of the tests that run, they all work with this PR as well!

Let me know if you want me to run them!

@ylacombe Sure!

Okay, cool! cc @LysandreJik for core maintainer review.

Hi @Rocketknight1,
I think return_attention_mask should also be added to chunk_iter function, so the warning will be removed even when we specify chunk_length_s.

cc @sanchit-gandhi - this PR just sets return_attention_mask=True on the preprocessors in the automatic_speech_recognition pipeline to avoid warnings caused by missing attention masks. It works okay in my testing, but please let me know if you think it could cause any problems!

Fixes #33498

Hi, where can I set return_attention_mask=True?

I'm having the error fixed here using latest transformers==4.47.1:

The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.

when running this code:

        pipe = pipeline(
            "automatic-speech-recognition",
            model=self.model,
            torch_dtype=torch.float16,
            chunk_length_s=30,
            batch_size=24,
            return_timestamps=True,
            device=self.device,
            tokenizer=processor.tokenizer,
            feature_extractor=processor.feature_extractor,
            model_kwargs={"use_flash_attention_2": True},
            generate_kwargs={
                "max_new_tokens": 128,
            },
        )