Contrastive Loss: Runtime error for certain audio files when reshaping out_masked_only
**Describe the bug**

After a few steps when pretraining a `SpeechEncDecSelfSupervisedModel`, training fails with the following error

```sh
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/ssl_models.py", line 468, in training_step
  loss_value, loss_val_dict = self.decoder_loss_step(
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/ssl_models.py", line 450, in decoder_loss_step
  current_loss_value = current_loss(
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1129, in _call_impl
  return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py", line 963, in __call__
  outputs = wrapped(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/losses/ssl_losses/contrastive.py", line 187, in forward
  out_masked_only = out_masked_only.reshape(bs, -1, out_masked_only.shape[-1])
RuntimeError: shape '[16, -1, 128]' is invalid for input of size 547712
```

**Steps/Code to reproduce bug**

I used the [speech_pre_training.py](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_pretraining/speech_pre_training.py) script with the default configuration for the conformer [here](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/ssl/conformer/conformer_ssl.yaml).

I only modified `train_ds.max_duration: 25.0`.
I also tried the config in the [Self_Supervised_Pre_Training.ipynb](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Self_Supervised_Pre_Training.ipynb) notebook and I got the same error.

**Expected behavior**

Training should process normally and/or a more detailed explanation why the reshape failed and what parameters to change.

**Environment overview (please complete the following information)**

 - Environment location: Docker (`nvcr.io/nvidia/pytorch:22.05-py3`)
 - Method of NeMo install: `pip install 'nemo_toolkit[all]==1.10.0'`
 - If method of install is [Docker], provide `docker pull` & `docker run` commands used
 
```sh
docker run --rm -it --gpus all --ipc=host --env-file .env train
```
