feat: Added the QAT finetuning script
Signed-off-by: Anurag Dixit <a.dixit91@gmail.com>

# What does this PR do ?
Adds a QAT finetuning workflow for ASR models.

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]
ASR 
# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
TRAIN_MANIFEST=<path to manifest file for training dataset>
EVAL_MANIFEST=<path to manifest file for evaluation dataset>
CONF_PATH=<path to model configuration>
NUM_GPU=<number of gpus>
NUM_NODES=<number of nodes>
BATCH_SIZE=<batch size>
LEARNING_RATE=0.00015
NUM_WORKERS=32
QUANT_MODEL=<Path to quantized model>
SAVE_MODEL=<Path to save model>
TOKENIZER_PATH=<Path to tokenizer>

python speech_to_text_qat.py \
--config-path=${CONF_PATH} \
--config-name=<config file name for model> \
model.train_ds.manifest_filepath=${TRAIN_MANIFEST} \
model.train_ds.batch_size=${BATCH_SIZE} \
model.validation_ds.manifest_filepath=${EVAL_MANIFEST} \
+model.train_ds.normalize=false \
++model.train_ds.num_workers=${NUM_WORKERS} \
++model.validation_ds.num_workers=${NUM_WORKERS} \
++model.train_ds.pin_memory=True \
++model.validation_ds.pin_memory=True \
+use_cer=false \
+trainer.devices=${NUM_GPU} \
trainer.num_nodes=${NUM_NODES} \
hydra.run.dir=${PWD} \
trainer.log_every_n_steps=50 \
+save_model=${SAVE_MODEL} \
+quantized_model=${QUANT_MODEL} \
++model.optim.lr=${LEARNING_RATE} \
++model.tokenizer.dir=${TOKENIZER_PATH} \
++model.tokenizer.type='bpe'
```

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**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
