num2words2 converts numbers like 42 to words like forty-two across 120+ languages and 170+ locale codes (see REFERENCE.md for the full table), producing cardinals, ordinals, currency, year, fractions, bank-cheque format, and aviation/ICAO digit-by-digit phraseology.
It is a Rust port of the num2words conversion engine with a thin Python binder. Every conversion runs in a compiled Rust core (PyO3/abi3); Python only normalises the arguments and shapes the result. The output is byte-for-byte identical to the original pure-Python library — validated against a frozen ~150,000-case corpus — while running typically 4–12× faster, and up to 26× on some languages and 150× on string parsing (see Performance).
The project is hosted on GitHub, and the full documentation is available in the Wiki. Contributions are welcome.
Performance
Because the conversion engine is compiled Rust, num2words2 is several times faster than the original pure-Python num2words. Benchmarked against num2words 0.5.14, Apple M-series, nanoseconds per call:
Operation |
original |
num2words2 |
speedup |
|---|---|---|---|
int → cardinal (8765) |
24,384 ns |
2,099 ns |
11.6× |
float → cardinal (1234.56) |
33,107 ns |
3,367 ns |
9.8× |
ordinal |
24,969 ns |
2,407 ns |
10.4× |
currency |
32,518 ns |
3,345 ns |
9.7× |
year |
13,484 ns |
1,434 ns |
9.4× |
string input ("1234") |
335,862 ns |
2,200 ns |
152.6× |
French cardinal |
61,046 ns |
2,359 ns |
25.9× |
German cardinal |
63,046 ns |
2,386 ns |
26.4× |
Russian cardinal |
4,781 ns |
1,001 ns |
4.8× |
The speedup is largest where the original does heavy Python-level parsing (string inputs) or deep recursion (French, German); it never comes at the cost of accuracy — output is byte-for-byte identical to the original across a frozen ~150,000-case corpus of cardinals, ordinals, currency, years, fractions, floats and strings.
Installation
Install from PyPI — the wheels bundle the Python binder and the compiled Rust extension, built per platform:
pip install num2words2
Wheels for every version are also attached to the GitHub Releases page; the compiled extension is distributed only through those release wheels (it is not committed to the repository).
To build from source you need a stable Rust toolchain and maturin:
maturin build --release
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 testRun 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 the full documentation, including installation, API details, CLI usage, supported locales, sentence conversion, currency handling, and migration guidance, please check the Wiki. Feel free to propose wiki enhancements.
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.
Relationship to num2words
num2words2 is not a divergent fork. It is a Rust port of the num2words conversion engine with a Python binder, kept output-compatible with the original.
The original project’s issues and pull requests are actively monitored. Valid, relevant fixes and features are reviewed and then ported into this project’s Rust core, with byte-parity re-verified against the frozen corpus. num2words2 therefore tracks upstream improvements while delivering them natively — and several times faster.
Jean-Louis Queguiner
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