CER estimation speedup for audio-based text normalization

[vsl9]

What does this PR do ?

The PR replaces jiwer with editdistance package to speedup CER estimation in audio-based TN.

Before your PR is "Ready for review"

Pre checks:

• Have you signed your commits? Use git commit -s to sign.
• Do all unittests finish successfully before sending PR?
  1. pytest or (if your machine does not have GPU) pytest --cpu from the root folder (given you marked your test cases accordingly @pytest.mark.run_only_on('CPU')).
  2. Sparrowhawk tests bash tools/text_processing_deployment/export_grammars.sh --MODE=test ...
• If you are adding a new feature: Have you added test cases for both pytest and Sparrowhawk here.
• Have you added __init__.py for every folder and subfolder, including data folder which has .TSV files?
• Have you followed codeQL results and removed unused variables and imports (report is at the bottom of the PR in github review box) ?
• Have you added the correct license header Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. to all newly added Python files?
• If you copied nemo_text_processing/text_normalization/en/graph_utils.py your header's second line should be Copyright 2015 and onwards Google, Inc.. See an example here.
• Remove import guards (try import: ... except: ...) if not already done.
• If you added a new language or a new feature please update the NeMo documentation (lives in different repo).
• Have you added your language support to tools/text_processing_deployment/pynini_export.py.

PR Type:

• New Feature
• Bugfix
• Documentation
• Test

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

[ekmb]

Thank you!