[feature request] [proposal] Always track CER in addition to WER for speech recognition tasks
I support this change. Can you share code for how you validate using CER? I'm using the validate code from the ASR with NeMo tutorial and it does something like 

```
for test_batch in pb(model.test_dataloader()):
          test_batch = [x.cuda() for x in test_batch]
          xs, x_lens, ys, y_lens = test_batch
          log_probs, encoded_len, greedy_predictions = model(
              input_signal=xs, input_signal_length=x_lens)
          # Notice the model has a helper object to compute WER
          model._wer.update(greedy_predictions, ys, y_lens)
          _, wer_num, wer_denom = quartznet._wer.compute()
          wer_nums.append(wer_num.detach().cpu().numpy())
          wer_denoms.append(wer_denom.detach().cpu().numpy())
```

and since `_WER` is already instantiated in quartznet I'm not sure how to turn CER on.  I think you need to pass `use_cer = True` to `_wer` constructor, maybe this can be configured via the yaml config. Unfortunately, I don't have the final code, as we found it's not practical to have just WER xor CER.

What you could do is to compute the CER a-posteriori, but for that you need to change logging to include the epoch/iteration number. That could be a separate request :) I figured this out and will note it here in case anyone wants to use CER in the future. Note `quartznet` is the variable name of my model so you may need to change it if your model has a different name. 

The easiest way is to toggle between CER and WER by setting `quartznet._wer.use_cer`,  
`quartznet._wer.use_cer=True` will switch it to use CER, `quartznet._wer.use_cer=False`  will switch it back to WER. 

Here is what the constructor looks like for WER and how you can override
```
from nemo.collections.asr.metrics.wer import WER
quartznet._wer = WER(
            vocabulary=quartznet.decoder.vocabulary,
            batch_dim_index=0,
            use_cer=True,
            ctc_decode=True,
            dist_sync_on_step=True,
            log_prediction=True,
        )
```

Since `log_every_n_steps` doesnt appear to apply to validation as discussed in #1518, you can use this to turn off the output logs if printing every single line is causing problems. This can also be done directly with `quartznet._wer.log_prediction=False` 

Hope this helps someone!

Edit: Also would like to say I'm very much in favor of including WER and CER by default.  Since .metrics are calculates on cpu we prefer to preserve only one metric during training. Offline it is much easier to evaluate models with large batch sizes as well. 
Currently one has to choose whether `use_cer` or not, and the choice is exclusive. Both are useful as the target metric, CER is more smooth.