 Evaluation    This code snippet shows how to evaluate **patrickvonplaten/wav2vec2-base-960h-4-gram** on LibriSpeech's "clean" and "other" test data.   ```python from datasets import load_dataset from transformers import AutoModelForCTC, AutoProcessor import torch from jiwer import wer  model_id = "patrickvonplaten/wav2vec2-base-960h-4-gram"  librispeech_eval = load_dataset("librispeech_asr", "other", split="test")  model = AutoModelForCTC.from_pretrained(model_id).to("cuda") processor = AutoProcessor.from_pretrained(model_id)  def map_to_pred(batch):     inputs = processor(batch["audio"]["array"], sampling_rate=16_000, return_tensors="pt")      inputs = {k: v.to("cuda") for k,v in inputs.items()}      with torch.no_grad():         logits = model(**inputs).logits      transcription = processor.batch_decode(logits.cpu().numpy()).text[0]     batch["transcription"] = transcription     return batch  result = librispeech_eval.map(map_to_pred, remove_columns=["audio"])  print(wer(result["text"], result["transcription"])) ```  *Result (WER)*:  | "clean" | "other" | |---|---| | 2.59 | 6.46 |