words2num2 0.2.1

Inverse of num2words2: convert spoken-form numbers back to numeric values across 100+ languages.

The inverse of num2words2.

Convert spoken-form numbers (“forty-two”, “trois cent quatre”, “二十三”) back into numeric values across 100+ locales.

• Hand-written English grammar parser: cardinals, ordinals, decimals, negatives, scale words up to centillion, year forms, “and” conjunctions, hyphenation, ASR-style outputs.

• Generic backend for every other locale supported by num2words2, derived automatically by reverse-mapping the forward conversion.

• Sentence-level mode that walks running text and replaces every word-number with its numeric form, preserving punctuation and surrounding text.

Note

This is the inverse direction of num2words2. For numbers → words see https://github.com/jqueguiner/num2words2.

Installation

pip install words2num2

num2words2 is a runtime dependency for the generic multi-language backend.

Usage

>>> from words2num2 import words2num, words2num_sentence
>>> words2num("forty-two")
42
>>> words2num("one thousand two hundred thirty-four")
1234
>>> words2num("minus seven")
-7
>>> words2num("three point one four")
Decimal('3.14')
>>> words2num("nineteen ninety nine", to="year")
1999
>>> words2num("twenty-first", to="ordinal")
21
>>> words2num("quarante-deux", lang="fr")
42
>>> words2num("zweiundvierzig", lang="de")
42
>>> words2num("сорок два", lang="ru")
42

>>> words2num_sentence("I bought twenty-three apples and fourteen pears.")
'I bought 23 apples and 14 pears.'

Auto-parse: numbers + currencies + units + locales

auto_parse extracts a numeric value plus its unit from any free-text expression. auto_parse_sentence walks running text and replaces every quantity in place. Supports configurable thousands/decimal separators per locale, currency symbols ($€£¥₹₽₩₺) and ISO codes (USD, EUR, GBP, …), scale shortcuts ($5m, $1.5b), SI/imperial units (length/mass/temperature/time/volume), percent, and disambiguation hints.

>>> from words2num2 import auto_parse, auto_parse_sentence

>>> auto_parse("$12,345.00")
Quantity(value=12345.0, unit='USD', kind='currency', confidence=1.0)
>>> auto_parse("$5m").value
5000000
>>> auto_parse("5cm")
Quantity(value=5, unit='cm', kind='length', confidence=1.0)
>>> auto_parse("20°C").kind
'temperature'
>>> auto_parse("forty-two kg").value
42

# Configurable separators
>>> auto_parse("1.234,56", lang="de").value
1234.56
>>> auto_parse("1 234,56", lang="fr").value
1234.56

# Disambiguation
>>> auto_parse("5m", prefer={"m": "mile"}).unit_long
'mile'

# Sentence mode
>>> auto_parse_sentence("Pay $12.50 for 5kg of apples at -5°C.")
'Pay 12.5 USD for 5 kg of apples at -5 °C.'
>>> auto_parse_sentence("Pay $12.50 for 5kg.", expand=True)
'Pay 12.5 dollar for 5 kilogram.'

Command-line

$ words2num2 "forty-two"
42
$ words2num2 "trois cent quatre" --lang=fr
304
$ words2num2 "twenty-third" --to=ordinal
23

Supported locales

words2num2 mirrors num2words2’s locale list — 100+ entries including:

af, am, ar, as, az, ba, be, bg, bn, bo, br, bs, ca, ce, cs, cy, da, de, el, en, en_IN, en_NG, eo, es, es_CO, es_CR, es_GT, es_NI, es_VE, et, eu, fa, fi, fo, fr, fr_BE, fr_CH, fr_DZ, gl, gu, ha, haw, he, hi, hr, ht, hu, hy, id, is, it, ja, jw, ka, kk, km, kn, ko, kz, la, lb, ln, lo, lt, lv, mg, mi, mk, ml, mn, mr, ms, mt, my, ne, nl, nn, no, oc, pa, pl, ps, pt, pt_BR, ro, ru, sa, sd, si, sk, sl, sn, so, sq, sr, su, sv, sw, ta, te, tet, tg, th, tk, tl, tr, tt, uk, ur, uz, vi, wo, yi, yo, zh, zh_CN, zh_HK, zh_TW

Aliases: jp → ja, cn → zh_CN.

API

words2num(text, lang="en", to="cardinal")

Parse text and return int, float, or Decimal. to is one of cardinal, ordinal, ordinal_num, year, currency.

words2num_sentence(text, lang="en", to="cardinal")

Walk a sentence, replacing every word-number with its numeric form. Returns a string. Aliases: convert_sentence, sentence_to_words.

How it works

• English (lang_EN) ships a hand-written recursive-descent parser that handles the full grammar — including ordinal/cardinal mixing, decimals, year mode, “a hundred” filler, and negative tokens.

• Every other locale uses Words2Num_Base, which lazily builds a {normalized_words: integer} table by calling num2words2 for each integer in a configurable range (defaults to -1..10000). This guarantees correctness for the lookup window for every locale supported upstream — at the cost of out-of-range values raising Words2NumError until a hand-written parser is added.

Hand-written parsers can be added incrementally per locale by overriding to_cardinal / to_ordinal in the corresponding lang_XX.py module — same pattern as num2words2.

Development

make install-dev
make test
make lint
make format

License

LGPL-2.1, mirroring num2words2.