Cannot get WER during WavVec2 fine-tuning
### System Info


- `transformers` version: 4.21.1
- Platform: Linux-5.15.0-43-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.8.1
- PyTorch version (GPU?): 1.12.0+cu116 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: no

### Who can help?

@patrickvonplaten, @anton-l

### Information

- [X] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

I followed the [blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) and revisited one of my old project, but I couldn't get WER during the fine-tuning. Surprisingly it worked perfectly early this year. I did get the following evaluation metrics during the first round:

```
{'eval_loss': -0.26198065280914307, 'eval_runtime': 41.4222, 'eval_samples_per_second': 25.904, 'eval_steps_per_second': 6.494, 'epoch': 0.27}
```

As you can see, there was no `eval_wer` in this entry.  Tried the following, still not seeing `eval_wer`

```python
def compute_metrics(pred):
    """
    batchfy and compute the WER metrics

    :param pred: _description_
    :type pred: _type_
    :return: _description_
    :rtype: _type_
    """
    wer_metric = load_metric("wer")
    pred_logits = pred.predictions # change to pred.logits did not help
    pred_ids = np.argmax(pred_logits, axis=-1)
    pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
    pred_str = processor.batch_decode(pred_ids)
    # we do not want to group tokens when computing the metrics
    label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
    wer = wer_metric.compute(predictions=pred_str, references=label_str)
    return {"wer": wer}



for batch in trainer.get_eval_dataloader():
    print(batch.keys())
    # return dict_keys(['input_values', 'labels'])
    batch = {k: v.to("cuda") for k, v in batch.items()}
    print(trainer.evaluate())
    # return {'eval_loss': 0.29788103699684143, 'eval_runtime': 44.4312, 'eval_samples_per_second': 24.15, 'eval_steps_per_second': 6.054}
    break
```

Any suggestions? Thanks!



### Expected behavior

Should return `eval_wer` in the evaluation step.