Python utilities for SignWriting.
pip install git+https://github.com/sign-language-processing/signwritingThis module provides utilities for converting between different formats of SignWriting. We include a few examples:
- To parse an FSW string into a
Signobject, representing the sign as a dictionary:
from signwriting.formats.fsw_to_sign import fsw_to_sign
fsw_to_sign("M123x456S1f720487x492")
# {'box': {'symbol': 'M', 'position': (123, 456)}, 'symbols': [{'symbol': 'S1f720', 'position': (487, 492)}]}- To convert a SignWriting string in SWU format to FSW format:
from signwriting.formats.swu_to_fsw import swu2fsw
swu2fsw('𝠃𝤟𝤩𝣵𝤐𝤇𝣤𝤐𝤆𝣮𝣭')
# M525x535S2e748483x510S10011501x466S2e704510x500S10019476x475This module provides utilities for tokenizing SignWriting strings for use in NLP tasks1. We include a few usage non-exhaustive examples:
- To tokenize a SignWriting string into a list of tokens:
from signwriting.tokenizer import SignWritingTokenizer
tokenizer = SignWritingTokenizer()
fsw = 'M123x456S1f720487x492S1f720487x492'
tokens = list(tokenizer.text_to_tokens(fsw, box_position=True))
# ['M', 'p123', 'p456', 'S1f7', 'c2', 'r0', 'p487', 'p492', 'S1f7', 'c2', 'r0', 'p487', 'p492'])- To convert a list of tokens back to a SignWriting string:
tokenizer.tokens_to_text(tokens)
# M123x456S1f720487x492S1f720487x492- For machine learning purposes, we can convert the tokens to a list of integers:
tokenizer.tokenize(fsw, bos=False, eos=False)
# [6, 932, 932, 255, 678, 660, 919, 924, 255, 678, 660, 919, 924]- Or to remove 'A' information, and separate signs by spaces, we can use:
from signwriting.tokenizer import normalize_signwriting
normalize_signwriting(fsw)This module is used to visualize SignWriting strings as images. Unlike sutton-signwriting/font-db which it is based on, this module does not support custom styling. Benchmarks show that this module is ~5000x faster than the original implementation.
from signwriting.visualizer.visualize import signwriting_to_image
fsw = "AS10011S10019S2e704S2e748M525x535S2e748483x510S10011501x466S20544510x500S10019476x475"
signwriting_to_image(fsw)
To use the visualizer with the server, you can hit: https://signwriting-sxie2r74ua-uc.a.run.app//visualizer?fsw=M525x535S2e748483x510S10011501x466S2e704510x500S10019476x475
This module includes general utilities that were not covered in the other modules.
join_signsjoins a list of signs into a single sign. This is useful for example for fingerspelling words out of individual character signs.
from signwriting.utils.join_signs import join_signs_vertical
char_a = 'M507x507S1f720487x492'
char_b = 'M507x507S14720493x485'
result_sign = join_signs_vertical(char_a, char_b)
# M510x518S1f720490x481S14720496x496This module is used to generate spelling data from a list of characters.
from signwriting.fingerspelling.fingerspelling import spell
word = "Hello" # any string of characters
language = "en-us-ase-asl" # long language code, as defined in the fingerspelling README
spell(word, language)
# M515x563S11502477x437S14a20492x457S1dc20484x477S1dc20484x512S17620492x547To use the fingerspelling with the server, you can hit: https://signwriting-sxie2r74ua-uc.a.run.app//fingerspelling?text=hello&signed_language=ase
This module is used to generate SpeechWriting from spoken words.
from signwriting.mouthing.mouthing import mouth
word = "Hello" # any string of characters, preferably valid words
language = "eng-Latn" # supported languages under "Language Support" at https://pypi.org/project/epitran/
mouth(word, language)
# M557x518S34700443x482S35c00469x482S34400495x482S34d00521x482Note: Installing English support for epitran requires extra steps,
see "Install flite" at mouthing/README.md.
To use the mouthing with the server, you can hit: https://signwriting-sxie2r74ua-uc.a.run.app//mouthing?text=hello&spoken_language=eng-Latn
@misc{moryossef2024-signwriting,
title={Utilities for SignWriting},
author={Moryossef, Amit},
howpublished={\url{https://github.com/sign-language-processing/signwriting}},
year={2024}
}Footnotes
-
Amit Moryossef, Zifan Jiang. ↩