VietNormalizer: An Open-Source, Dependency-Free Python Library for Vietnamese Text Normalization in TTS and NLP Applications

2026-03-04

AI Summary

This work addresses the challenge posed by prevalent non-standard textual elements in Vietnamese—such as numerals, dates, currencies, abbreviations, and loanwords—which hinder the effective processing of text-to-speech (TTS) and natural language processing (NLP) systems. Existing normalization tools are often either computationally heavy, incompletely covered, or dependent on external services, making standalone deployment impractical. To overcome these limitations, we propose a lightweight, zero-dependency, rule-based unified text normalization pipeline that integrates precompiled regular expressions, a rule engine, CSV-based dictionary mappings, transliteration algorithms, and Unicode normalization. Notably, the system operates without neural networks or external APIs yet comprehensively handles diverse non-standard tokens. The solution is open-sourced via PyPI and GitHub, supports pip installation, and offers high throughput, low memory footprint, and strong extensibility, providing the first self-contained text normalization framework tailored for low-resource tonal languages like Vietnamese.

Technology Category

• Natural Language Processing: Speech
• Computer Vision: Language and Vision
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

We present VietNormalizer1, an open-source, zero-dependency Python library for Vietnamese text normalization targeting Text-to-Speech (TTS) and Natural Language Processing (NLP) applications. Vietnamese text normalization is a critical yet underserved preprocessing step: real-world Vietnamese text is densely populated with non-standard words (NSWs), including numbers, dates, times, currency amounts, percentages, acronyms, and foreign-language terms, all of which must be converted to fully pronounceable Vietnamese words before TTS synthesis or downstream language processing. Existing Vietnamese normalization tools either require heavy neural dependencies while covering only a narrow subset of NSW classes, or are embedded within larger NLP toolkits without standalone installability. VietNormalizer addresses these gaps through a unified, rule-based pipeline that: (1) converts arbitrary integers, decimals, and large numbers to Vietnamese words; (2) normalizes dates and times to their spoken Vietnamese forms; (3) handles VND and USD currency amounts; (4) expands percentages; (5) resolves acronyms via a customizable CSV dictionary; (6) transliterates non-Vietnamese loanwords and foreign terms to Vietnamese phonetic approximations; and (7) performs Unicode normalization and emoji/special-character removal. All regular expression patterns are pre-compiled at initialization, enabling high-throughput batch processing with minimal memory overhead and no GPU or external API dependency. The library is installable via pip install vietnormalizer, available on PyPI and GitHub at https://github.com/nghimestudio/vietnormalizer, and released under the MIT license. We discuss the design decisions, limitations of existing approaches, and the generalizability of the rule-based normalization paradigm to other low-resource tonal and agglutinative languages.

Problem

• Vietnamese text normalization
• non-standard words
• Text-to-Speech
• Natural Language Processing
• low-resource languages

Innovation

• text normalization
• rule-based pipeline
• zero-dependency
• Vietnamese NLP
• non-standard words