[Audio] SSL Pretraining framework for flow-matching model for audio processing
# What does this PR do ?

This PR adds a ssl-pretraining framework and a flow matching AudioToAudio model

**Collection**: audio

# Changelog 
- Add a flow matching generative model in `audio/models/enhancement.py`
- Create `audio/modules/ssl_pretrain_masking` to partially-masked out input in the ssl pre-training stage.
- Create `audio/parts/submodules/transformerunet.py` to implement a Transformer U-Net structure.
- Create `audio/parts/submodules/flow.py` for training/inferencing with the flow matching generative model
- Create `audio/parts/utils/callbacks.py` to add processed audio in the training logger.

# Usage

Model can be first be pre-trained using

```python
python examples/audio/audio_to_audio_train.py --config-name flow_matching_generative_ssl_pretraining.yaml
```
- Please update the `train_ds.manifest_filepath` and `validation_ds.manifest_filepath` with the correct filepaths

After the ssl-pretraining stage is completed, the pretrained model could be finetuned using 

```python
python examples/audio/audio_to_audio_train.py --config-name flow_matching_generative_finetuning.yaml
```
- Remember to update the `init_from_nemo_model` with the pretrained checkpoint path in the config file.

Alternatively, one can also just train a flow matching generative from scratch:
```python
python examples/audio/audio_to_audio_train.py --config-name flow_matching_generative.yaml
```

Model can be evaluated using
```python
python examples/audio/audio_to_audio_eval.py \
    model_path=${PATH_TO_NEMO_FILE} \
    dataset_manifest=${PATH_TO_DATASET_MANIFEST} \
    output_dir=${PATH_TO_OUTPUT_DIR} \
    input_key=noisy_filepath \
    target_key=clean_filepath \
    metrics=[sdr,estoi]
```

Sampler setting can be easily overridden, e.g., to change the number of sampling steps as

```python
python examples/audio/audio_to_audio_eval.py ... ++sampler.num_steps=${NUM_STEPS}
```


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