Incorrect WER reported by nemo wer function
On my test set when I run  trainer.test() I am getting  a wer of 0.081. 

 global_step        │            0.0            │
│         test_loss         │     7.94022274017334      │
│         test_wer          │    0.08155332505702972  


but when I try running 
jiwer.wer or 
from nemo.collections.asr.metrics.wer import word_error_rate 

I am getting wer == 0.139 why is there such a big difference. 


have also tried : 

WER = WERBPE(decoding=ctc_bpe_decoding, use_cer = False,log_prediction=False, fold_consecutive=False,dist_sync_on_step=True)

Even this gives me a wer of 0.139

I am running this function for every sample and then taking mean 


```
from nemo.collections.asr.metrics.wer_bpe import CTCBPEDecoding,  WERBPE
from nemo.collections.asr.metrics.wer_bpe import CTCDecodingConfig

ctc_bpe_decoding  = CTCBPEDecoding(decoding_cfg = CTCDecodingConfig(), tokenizer = asr_model.tokenizer)

asr_model._wer.log_predictions  = False
asr_model._wer.use_cer  = False

WER = WERBPE(decoding=ctc_bpe_decoding, use_cer = False,log_prediction=False, fold_consecutive=False,dist_sync_on_step=True)

@torch.no_grad()
def cal_wer(gt, pred):
    global WER
    # print(asr_model.tokenizer.text_to_tokens(gt))

    gt = asr_model.tokenizer.text_to_ids(gt)
    pred = asr_model.tokenizer.text_to_ids(pred)

    len_gt = torch.tensor([len(gt)])
    len_pred = torch.tensor([len(pred)])
    
    gt = torch.tensor([gt])
    pred = torch.tensor([pred])

    WER.update(targets = gt, predictions = pred, target_lengths = len_gt, predictions_lengths=len_pred)

    return WER.compute()[0].item()
```