Add support for RNNT Char/Word Timestamp Calculation
# What does this PR do ?

Adds support for RNNT models to compute character level and word level timestamps using similar steps to CTC models.

**Collection**: [ASR]

# Changelog 
- Adds char and word level timestamp calculation for character and subword models

# Usage

```python

# Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import pytorch_lightning as pl
from omegaconf import OmegaConf, open_dict

from nemo.collections.asr.models import ASRModel


def main():

    trainer = pl.Trainer(gpus=[0], precision=32, logger=False)

    # Char model
    # path = "Path to char model"

    # Subword model
    path = "Path to subword model"

    asr_model = ASRModel.restore_from(path, trainer=trainer, map_location='cpu')  # type: EncDecRNNTModel

    asr_model.eval()
    asr_model.freeze()

    asr_model.preprocessor.featurizer.dither = 0.0
    asr_model.preprocessor.featurizer.pad_to = 0

    decoding_cfg = OmegaConf.create({})

    with open_dict(decoding_cfg):
        decoding_cfg.strategy = "greedy_batch"
        decoding_cfg.preserve_alignments = True
        decoding_cfg.compute_timestamps = True
        decoding_cfg.rnnt_timestamp_type = "all"

    asr_model.change_decoding_strategy(decoding_cfg)

    hyp, _ = asr_model.transcribe(['/test-other-processed/367-130732-0003.wav'],
                   batch_size=1, return_hypotheses=True)

    print("Evaluating ---")
    print()
    print("Transcription: ", hyp[0].text)
    print()
    print("Total number of timesteps :", hyp[0].length)
    print()
    print("Character/Subword Time-Stamps :", hyp[0].timestep['char'])
    print()
    print("Word Time-Stamps :", hyp[0].timestep['word'])
    print()


if __name__ == '__main__':
    main()

```

# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [x] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
