NeMo no longer supports transcripts with diacritics
**Describe the bug**

I am training an Arabic model with [diacritics](https://en.wikipedia.org/wiki/Arabic_diacritics).
Digitally, each diacritic is represented as a separate (unicode) character from the actual letter.
[Here's](https://github.com/Tarteel-io/quran-assets/blob/main/text/vocab-DI.txt) an example of the vocabulary/text that we are using.
There are 10,878 unique words in the vocabulary (so large enough for a SPE tokenizer with a `vocab_size` of 1024).

I am able to generate a SPE-based tokenizer using our diacritized vocabulary.
I can confirm that the generated `document.txt` and `vocab.txt` for the tokenizer have Arabic text represented correctly.
However, somewhere during training the tokenizer fails to decode the text properly.

This is an example output from the `WER` metric:

```
[NeMo I 2022-03-04 09:48:36 wer_bpe:204] reference:غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇
[NeMo I 2022-03-04 09:48:36 wer_bpe:205] predicted:جعلنا غبعوثون الض تجري غ ض غ استكبروا غ استكبرواࣲ غ وللكافرين إ وإليه الض قلتم استكبروابعوثون غ وإلى أظلم استكبروا س استكبروا غ عنهم أحسن استكبروا جعلنا الض الجاه ]
تجري الهمد للذين غ استكبروا القرآن غر جعلنا غ رؤوس أصببعوثون ضآيات شديد شديد وإلى غࣲ استكبروا غ استكبروا غ جعلنا غ أحسن الض ألفافا أنفسكم والسماء آتينا لله غ ض آتينا غ جعلنا الض غ قلتم آتينا الجاه تجري غ وأخ جعلنا ]
⁇  ألفافا جعلنا غإ غ استكبروا وأخ وإلى وأخ غ إ غ جعلنا تجري وإلى استكبروابعوثون
```

Notice two things here:
1.  The `reference` text is only two characters: `⁇` and `غ`. 
2. The predicted text does not have any diacritics (despite the tokenizer and vocab having diacritics).
After a few epochs, the model "converges" and predicts only those two characters. Basically the loss goes to 0 in ~1k steps.

```
[NeMo I 2022-03-04 09:49:46 wer_bpe:204] reference:غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇
[NeMo I 2022-03-04 09:49:46 wer_bpe:205] predicted:غ ⁇  غ ⁇  جعلنا جعلنا ⁇  وإلى غ ⁇  ⁇  غ ⁇  غ ⁇  ⁇  غ ⁇  جعلنا عليها ⁇  ألفافا جعلنا ⁇  غ ⁇  غ ⁇  غ ⁇  غ ⁇  لله بأفواه
```

If I train a model without diacritics, the same behavior occurs. The last working NeMo version was `1.4.0` (will test with `1.5.0`).
Previously, the model would converge at a reasonable rate and achieves good results (~4% WER).

**Steps/Code to reproduce bug**

There's nothing really special about my training script, it's taken from the examples.
The model is finetuned from the `stt_en_citrinet_1024` model.
`cfg.model_target` is `nemo.collections.asr.models.EncDecCTCModelBPE`

```py
from omegaconf import OmegaConf
import pytorch_lightning as pl
import wandb

from nemo.collections.asr.models import ASRModel
from nemo.core.config import hydra_runner
from nemo.utils import logging, model_utils
from nemo.utils.exp_manager import exp_manager


@hydra_runner(config_path="conf/citrinet/", config_name="config")
def main(cfg):
    # Setup trainer and exp. manager
    trainer = pl.Trainer(**cfg.trainer)
    log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
    # Setup Model
    model_class = model_utils.import_class_by_path(cfg.model_target)  # type: ASRModel
    asr_model = model_class.from_pretrained(model_name=cfg.init_from_pretrained_model)
    asr_model.cfg = cfg.model
    asr_model.set_trainer(trainer)
    asr_model.setup_training_data(cfg.model.train_ds)
    asr_model.setup_multiple_validation_data(cfg.model.validation_ds)
    asr_model.setup_optimization(cfg.model.optim)
    # Setup Augmentation
    asr_model.spec_augmentation = asr_model.from_config_dict(cfg.model.spec_augment)
    # Change vocab
    asr_model.change_vocabulary(
        new_tokenizer_dir=cfg.model.tokenizer.dir,
        new_tokenizer_type=cfg.model.tokenizer.type
    )
    trainer.fit(asr_model)


if __name__ == '__main__':
    main()
```

The tokenizer is generated using [process_asr_text_tokenizer.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py):

```sh
python process_asr_text_tokenizer.py --manifest=<path to train manifest files, seperated by commas> \
         --data_root=tokenizers \
         --vocab_size=1024 \
         --tokenizer=spe \
         --log
```

**Expected behavior**

The model should be able to converge and predict Arabic text accurately.

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

 - Environment location: Docker (`nvcr.io/nvidia/pytorch:21.10-py3`)
 - Method of NeMo install: `pip install nemo_toolkit[all]==1.7.0`
 - If method of install is [Docker], provide `docker pull` & `docker run` commands used

Dockerfile:

```
FROM nvcr.io/nvidia/pytorch:21.10-py3

ARG DEBIAN_FRONTEND=noninteractive

COPY ./requirements.txt requirements.txt 

RUN apt update && \
    apt install -y ffmpeg libsndfile1 && \
    python3 -m pip install --upgrade pip && \
    python3 -m pip install -r requirements.txt
```

```sh
docker run --rm -it --gpus all --shm-size 64G --ipc=host --env-file .env -v /home/$USER:/home/$USER train
```

**Additional context**

GPU: 8xV100 (AWS p3dn.24xlarge)
