 Evaluation  This code snippet shows how to evaluate Whisper Large on [LibriSpeech test-clean](https://huggingface.co/datasets/librispeech_asr):   ```python >>> from datasets import load_dataset >>> from transformers import WhisperForConditionalGeneration, WhisperProcessor >>> import torch >>> from evaluate import load  >>> librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test")  >>> processor = WhisperProcessor.from_pretrained("openai/whisper-large") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large").to("cuda")  >>> def map_to_pred(batch): >>>     audio = batch["audio"] >>>     input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features >>>     batch["reference"] = processor.tokenizer._normalize(batch['text']) >>>  >>>     with torch.no_grad(): >>>         predicted_ids = model.generate(input_features.to("cuda"))[0] >>>     transcription = processor.decode(predicted_ids) >>>     batch["prediction"] = processor.tokenizer._normalize(transcription) >>>     return batch  >>> result = librispeech_test_clean.map(map_to_pred)  >>> wer = load("wer") >>> print(100 * wer.compute(references=result["reference"], predictions=result["prediction"])) 3.0003583080317572 ```  