GPU-based vectorized `SpecAug`
Closing in favor of https://github.com/NVIDIA/NeMo/pull/9155
# What does this PR do ?

Context: together with @galv we found feature normalization and specaug to take approx 30% of the total forward step time in Canary training, due to CPU-bottlenecked implementations. Feature normalization is addressed in #8964. 

This PR adds a GPU based SpecAugment. The original implementation loops over every example and every mask, then waits on CPU RNG to sample the numbers. The new fast implementation still applies masks sequentially, but is vectorized on batch size and uses GPU's RNG. We found approx. 5x speedup (70ms -> 17ms in profiling, but both numbers include profiler overhead). I also added a flag to be able to revert to the old implementation in case anybody encounters a compatibility issue.

I validated visually that the new impl behavior is as expected. 

Old:
<img width="1696" alt="image" src="https://github.com/NVIDIA/NeMo/assets/15930688/78c5b421-e2ba-4bc7-a121-20eab75f9c29">

New:
<img width="1696" alt="image" src="https://github.com/NVIDIA/NeMo/assets/15930688/31df5106-326b-4679-9ca0-3c9446c0a49e">

**Collection**: ASR

# Changelog 
- 5x faster SpecAugment to reduce typical forward step time by ~10%.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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# Additional Information
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