def METHOD_NAME(model):
    predictions = []
    references = []
    for batch in librispeech_test_clean:
        audio = batch["audio"]
        input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
        reference = processor.tokenizer._normalize(batch['text'])
        references.append(reference)
        with torch.no_grad():
            predicted_ids = model.generate(input_features)[0]
        transcription = processor.decode(predicted_ids)
        prediction = processor.tokenizer._normalize(transcription)
        predictions.append(prediction)
    wer_result = wer.compute(references=references, predictions=predictions)
    print(f"Result wer: {wer_result * 100}")
    accuracy = 1 - wer_result
    print("Accuracy: %.5f" % accuracy)
    return accuracy