Text (Inverse) Normalization#

The nemo_text_processing Python package [TEXTPROCESSING-NORM2] is based on WFST grammars [TEXTPROCESSING-NORM1] and supports:

  1. Text Normalization (TN) converts text from written form into its verbalized form. It is used as a preprocessing step before Text to Speech (TTS). For example,

"123" -> "one hundred twenty three"

  1. Inverse text normalization (ITN) is a part of the Automatic Speech Recognition (ASR) post-processing pipeline and can be used to convert normalized ASR model outputs into written form to improve text readability. For example,

"one hundred twenty three" -> "123"

  1. Audio-based provides multiple normalization options. For example,

"123" -> "one hundred twenty three", "one hundred and twenty three", "one two three", "one twenty three" ...

The normalization which best reflects what is actually said in an audio is then picked. Audio-based TN can be used to normalize ASR training data.

Text TN and ITN

Installation#

nemo_text_processing is automatically installed with NeMo.

Quick Start Guide#

Text Normalization#

cd NeMo/nemo_text_processing/text_normalization/
python normalize.py --text="123" --language=en

Inverse Text Normalization#

cd NeMo/nemo_text_processing/inverse_text_normalization/
python inverse_normalize.py --text="one hundred twenty three" --language=en

Arguments:

  • text - Input text.

  • input_file - Input file with lines of input text. Only one of text or input_file is accepted.

  • output_file - Output file to save normalizations. Needed if input_file is specified.

  • language - language id.

  • input_case - Only for text normalization. lower_cased or cased.

  • verbose - Outputs intermediate information.

  • cache_dir - Specifies a cache directory for compiled grammars. If grammars exist, this significantly improves speed.

  • overwrite_cache - Updates grammars in cache.

  • whitelist - TSV file with custom mappings of written text to spoken form.

Audio-based TN#

cd NeMo/nemo_text_processing/text_normalization/
python normalize_with_audio.py --text="123" --language="en" --n_tagged=10 --cache_dir="cache_dir" --audio_data="example.wav" --model="stt_en_conformer_ctc_large"

Additional Arguments:

  • text - Input text or JSON manifest file with multiple audio paths.

  • audio_data - (Optional) Input audio.

  • model - Off-shelf NeMo CTC ASR model name or path to local NeMo model checkpoint ending on .nemo

  • n_tagged - number of normalization options to output.

Note

More details can be found in NeMo/tutorials/text_processing/Text_(Inverse)_Normalization.ipynb in Google’s Colab.

Language Support Matrix#

Language

ID

TN

ITN

Audio-based TN

English

en

x

x

x

Spanish

es

x

x

x

German

de

x

x

x

French

fr

x

Russian

ru

x

x

Vietnamese

vi

x

Grammar customization#

Note

In-depth walk through NeMo/tutorials/text_processing/WFST_tutorial.ipynb in Google’s Colab.

Deploy to C++#

See Text Procesing Deployment for details.

References#

TEXTPROCESSING-NORM1

Mehryar Mohri, Fernando Pereira, and Michael Riley. Weighted automata in text and speech processing. arXiv preprint cs/0503077, 2005.

TEXTPROCESSING-NORM2

Yang Zhang, Evelina Bakhturina, Kyle Gorman, and Boris Ginsburg. Nemo inverse text normalization: from development to production. 2021. arXiv:2104.05055.