num2words2 1.0.16

Modern, actively maintained fork of num2words optimized for LLM/AI/speech applications.

num2words2 is a modern, actively maintained fork of the original num2words library that converts numbers like 42 to words like forty-two. It supports multiple languages (see the list below for full list of languages) and can even generate ordinal numbers like forty-second. This fork was created to address the maintenance gap in the original project and optimize for modern AI/LLM/speech applications.

The project is hosted on GitHub. Contributions are welcome.

Installation

The easiest way to install num2words2 is to use pip:

pip install num2words2

Otherwise, you can download the source package and then execute:

python setup.py install

Development Setup

The project uses pre-commit hooks to ensure code quality. To set up your development environment:

# Install pre-commit
pip install pre-commit

# Install the git hook scripts
pre-commit install

# Run hooks on all files (optional, useful for initial setup)
pre-commit run --all-files

This will automatically format and lint your code before each commit using:

• autopep8 - PEP 8 formatting

• autoflake - removes unused imports and variables

• isort - sorts imports

• flake8 - style and quality checks

• trailing-whitespace removal

• end-of-file fixing

Testing

The library uses pytest for testing. First, install the development dependencies:

make dev-install

Then, you can run the test suite using several methods:

• Run basic tests: This runs tests with your current Python environment.

  make test

• Run with Tox: This runs tests against all supported Python versions, which is the standard for CI.

  tox

Generating End-to-End Tests with LLMs

The repository includes a powerful script to generate high-quality, realistic test cases using Large Language Models (LLMs). This helps ensure accuracy across multiple languages and complex scenarios.

What it does: The tests/scripts/generate_llm_tests.py script uses an LLM (like GPT-4o) to create sentences containing numbers, dates, and currencies, and then generates the expected word-for-word conversion.

Requirements:

• An OpenAI API key. You must set it as an environment variable: export OPENAI_API_KEY='your-key-here'

How to Use:

To generate 10 new test sentences for French and Spanish, you can run:

python tests/scripts/generate_llm_tests.py --languages fr,es --samples 10

The new tests will be appended to tests/data/e2e_test_sentences.csv.

Key Options:

• --languages: Comma-separated list of language codes (e.g., en_IN,de,it).

• --samples: Number of samples to generate per language.

• --mode: Use sentences for full sentences or numbers for direct number-to-word conversions.

• --model: The OpenAI model to use (e.g., gpt-4o, gpt-4o-mini).

• --output: Specify a different output file.

• --overwrite: Overwrite the output file instead of appending.

This tool is essential for expanding test coverage and ensuring the library’s robustness.

Usage

Command line:

$ num2words2 10001
ten thousand and one
$ num2words2 24,120.10
twenty-four thousand, one hundred and twenty point one
$ num2words2 24,120.10 -l es
veinticuatro mil ciento veinte punto uno
$ num2words2 2.14 -l es --to currency
dos euros con catorce céntimos

In code there’s only one function to use:

>>> from num2words2 import num2words
>>> num2words(42)
forty-two
>>> num2words(42, to='ordinal')
forty-second
>>> num2words(42, lang='fr')
quarante-deux

Besides the numerical argument, there are two main optional arguments, to: and lang:

to: The converter to use. Supported values are:

• cardinal (default)

• ordinal

• ordinal_num

• year

• currency

lang: The language in which to convert the number. Supported values are:

• en (English, default)

• am (Amharic)

• ar (Arabic)

• az (Azerbaijani)

• be (Belarusian)

• bn (Bangladeshi)

• ca (Catalan)

• ce (Chechen)

• cs (Czech)

• cy (Welsh)

• da (Danish)

• de (German)

• en_GB (English - Great Britain)

• en_IN (English - India)

• en_NG (English - Nigeria)

• es (Spanish)

• es_CO (Spanish - Colombia)

• es_CR (Spanish - Costa Rica)

• es_GT (Spanish - Guatemala)

• es_VE (Spanish - Venezuela)

• eu (EURO)

• fa (Farsi)

• fi (Finnish)

• fr (French)

• fr_BE (French - Belgium)

• fr_CH (French - Switzerland)

• fr_DZ (French - Algeria)

• he (Hebrew)

• hi (Hindi)

• hu (Hungarian)

• hy (Armenian)

• id (Indonesian)

• is (Icelandic)

• it (Italian)

• ja (Japanese)

• kn (Kannada)

• ko (Korean)

• kz (Kazakh)

• mn (Mongolian)

• lt (Lithuanian)

• lv (Latvian)

• nl (Dutch)

• no (Norwegian)

• pl (Polish)

• pt (Portuguese)

• pt_BR (Portuguese - Brazilian)

• ro (Romanian)

• ru (Russian)

• sl (Slovene)

• sk (Slovak)

• sr (Serbian)

• sv (Swedish)

• te (Telugu)

• tet (Tetum)

• tg (Tajik)

• tr (Turkish)

• th (Thai)

• uk (Ukrainian)

• vi (Vietnamese)

• zh (Chinese - Traditional)

• zh_CN (Chinese - Simplified / Mainland China)

• zh_TW (Chinese - Traditional / Taiwan)

• zh_HK (Chinese - Traditional / Hong Kong)

You can supply values like fr_FR; if the country doesn’t exist but the language does, the code will fall back to the base language (i.e. fr). If you supply an unsupported language, NotImplementedError is raised. Therefore, if you want to call num2words with a fallback, you can do:

try:
    return num2words(42, lang=mylang)
except NotImplementedError:
    return num2words(42, lang='en')

Additionally, some converters and languages support other optional arguments that are needed to make the converter useful in practice.

Wiki

For additional information on some localization please check the Wiki. And feel free to propose wiki enhancement.

History

num2words is based on an old library, pynum2word, created by Taro Ogawa in 2003. Unfortunately, the library stopped being maintained and the author can’t be reached. There was another developer, Marius Grigaitis, who in 2011 added Lithuanian support, but didn’t take over maintenance of the project.

Virgil Dupras from Savoir-faire Linux based himself on Marius Grigaitis’ improvements and re-published pynum2word as num2words.

num2words2 Fork

num2words2 is a modern fork of the original num2words library, created to address the maintenance gap and optimize for modern AI/LLM/speech applications. This fork:

• Provides active maintenance aligned with rapidly evolving AI/ML ecosystem

• Fixes critical bugs affecting machine learning pipelines

• Adds enhanced language support for global AI applications

• Maintains backward compatibility with the original library

Jean-Louis Queguiner