Quickstart¶
Installation¶
text2num is available as precompiled wheel for Linux, MacOS and Windows operating systems, and Python
versions 3.8 up to 3.13 (included).
To install text2num in your (virtual) environment:
pip install text2num
Or if you manage your projects with uv:
uv add text2num
That’s all folks!
Usage¶
by example.
Parse and convert¶
Integers only.
>>> from text_to_num import text2num
>>> text2num("fifty-one million five hundred seventy-eight thousand three hundred two", "en")
51578302
>>> text2num("eighty-one", "en")
81
>>> text2num("ochenta y uno", "es")
81
>>> text2num("nueve mil novecientos noventa y nueve", "es")
9999
>>> text2num('quatre-vingt-quinze', "fr")
95
>>> text2num("cinquante et un million cinq cent soixante dix-huit mille trois cent deux", "fr")
51578302
>>> text2num('thousand thousand and two hundreds', 'en')
Traceback (most recent call last):
...
ValueError: invalid literal for text2num: 'thousand thousand and two hundreds'
Find and transcribe¶
Any number, even ordinals.
>>> from text_to_num import alpha2digit
>>> text = "On May twenty-third, I bought twenty-five cows, twelve chickens and one hundred twenty five point five kg of potatoes."
>>> alpha2digit(text, "en")
'On May 23rd, I bought 25 cows, 12 chickens and 125.5 kg of potatoes.'
>>> alpha2digit("I finished the race in the twelfth position!", "en")
'I finished the race in the 12th position!'
Both 'coma' and 'punto' are supported for Spanish:
>>> text = "Compramos veinticinco vacas, doce gallinas y ciento veinticinco coma cuarenta kg de patatas."
>>> alpha2digit(text, "es")
'Compramos 25 vacas, 12 gallinas y 125,40 kg de patatas.'
>>> text = "Compramos veinticinco vacas, doce gallinas y ciento veinticinco punto cuarenta kg de patatas."
>>> alpha2digit(text, "es")
'Compramos 25 vacas, 12 gallinas y 125.40 kg de patatas.'
>>> sentence = (
... "Huit cent quarante-deux pommes, vingt-cinq chiens, mille trois chevaux, "
... "douze mille six cent quatre-vingt-dix-huit clous.\n"
... "Quatre-vingt-quinze vaut nonante-cinq. On tolère l'absence de tirets avant les unités : "
... "soixante seize vaut septante six.\n"
... "Nombres en série : douze, quinze, zéro zéro quatre, vingt, cinquante-deux, cent trois, cinquante deux, "
... "trente et un.\n"
... "Ordinaux: cinquième troisième vingt et unième centième mille deux cent trentième.\n"
... "Décimaux: douze virgule quatre-vingt dix-neuf, cent vingt virgule zéro cinq ; "
... "mais soixante zéro deux."
... )
>>> print(alpha2digit(sentence, "fr"))
842 pommes, 25 chiens, 1003 chevaux, 12698 clous.
95 vaut 95. On tolère l'absence de tirets avant les unités : 76 vaut 76.
Nombres en série : 12, 15, 004, 20, 52, 103, 52, 31.
Ordinaux: 5ème 3ème 21ème 100ème 1230ème.
Décimaux: 12,99, 120,05 ; mais 60 02.
>>> print(alpha2digit("on distingue aussi l'article (un chat) du nombre: un deux trois.", "fr"))
on distingue aussi l'article (un chat) du nombre: 1 2 3.
Working with tokens¶
Imagine that we have an ASR application that returns a transcript as a list of tokens (text, start timestamp, end timestamp) where the timestamps are intergers reprensenting milliseconds relative to the beginning of the speech.
from text_to_num import (Token, find_numbers)
class DecodedWord(Token):
def __init__(self, text, start, end):
self._text = text
self.start = start
self.end = end
def text(self):
return self._text
def nt_separated(self, previous):
# we consider a voice gap of more that 100 ms as significant
return self.start - previous.end > 100
# Let's simulate ASR output
stream = [
DecodedWord("We", 0, 100),
DecodedWord("have", 100, 200),
DecodedWord("respectively", 200, 400),
DecodedWord("twenty", 400, 500),
DecodedWord("nine", 610, 700),
DecodedWord("and", 700, 800),
DecodedWord("thirty", 800, 900),
DecodedWord("four", 950, 1000),
DecodedWord("dollars", 1010, 1410)
]
occurences = find_numbers(stream, "en")
for num in occurences:
print(f"found number {num.text} ({num.value}) at range [{num.start}, {num.end}] in the stream")
When executed, that code snippet prints:
found number 20 (20.0) at range [3, 4] in the stream
found number 9 (9.0) at range [4, 5] in the stream
found number 34 (34.0) at range [6, 8] in the stream