transformers.AutomaticSpeechRecognitionPipeline)  method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. It can also be extended to  predict utterance level timestamps by passing `return_timestamps=True`:  ```python >>> import torch >>> from transformers import pipeline >>> from datasets import load_dataset  >>> device = "cuda:0" if torch.cuda.is_available() else "cpu"  >>> pipe = pipeline( >>>   "automatic-speech-recognition", >>>   model="openai/whisper-small.en", >>>   chunk_length_s=30, >>>   device=device, >>> )  >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"]  >>> prediction = pipe(sample.copy())["text"] " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."  >>> 