Inconsistent model inference
**Describe the bug**

Outputs different predictions for the same input depending batch. I suspect the problem is related to pad tokens not being masked properly.

**Steps/Code to reproduce bug**

```
import nemo.collections.asr as nemo_asr

model = nemo_asr.models.ASRModel.from_pretrained(model_name="stt_en_conformer_ctc_large")
model.transcribe(audio_paths[:2], batch_size=1)

# output
# ['take care of over excitement and endeavor to keep a quiet mind even for your health it is the best advice that can be given to you # # your moral and spiritual improvement will then keep pace with the culture of your intellectual powers',
# 'ab crowd']

model.transcribe(audio_paths[:2], batch_size=2)
# output
# ['take care of over excitement and endeavor to keep a quiet mind even for your health it is the best advice that can be given to you # # your moral and spiritual improvement will then keep pace with the culture of your intellectual powers',
# 'crow']

# the provided example occurred with audio files from the libritts100 dataset namely (3575_170457_000032_000001.wav and 6829_68771_000042_000001.wav), but I am quite sure it can be reproduced with other audio files as well. 
```

**Expected behavior**

Outputs should be identical independent of batch size or items in batch.

**Environment overview**

Linux Ubuntu 20.04
Python version: 3.8
PyTorch version: 1.10.0+cu113
Nemo version: 1.4.0
CUDA/cuDNN version: 11
GPU model and memory: RTX 3070, Tesla V100-SXM2-16GB
