WorldAlphabets

A tool to access alphabets of the world with Python and Node interfaces.

Usage

Python

Install the package:

pip install worldalphabets

To load the data in Python (omitting script uses the first script listed):

from worldalphabets import get_available_codes, get_scripts, load_alphabet

codes = get_available_codes()
print("Loaded", len(codes), "alphabets")

alphabet = load_alphabet("en")  # defaults to first script (Latn)
print("English uppercase:", alphabet.uppercase[:5])
print("English digits:", alphabet.digits)

scripts = get_scripts("mr")
print("Marathi scripts:", scripts)

alphabet_mr = load_alphabet("mr", script=scripts[0])
print("Marathi uppercase:", alphabet_mr.uppercase[:5])
print("Marathi frequency for 'a':", alphabet_mr.frequency["a"])

# Example with Arabic digits
alphabet_ar = load_alphabet("ar", "Arab")
print("Arabic digits:", alphabet_ar.digits)

# Language detection (see Language Detection section for details)
from worldalphabets import optimized_detect_languages
results = optimized_detect_languages("Hello world")  # Automatic detection
print("Detected languages:", results)

Node.js

From npm

Install the package from npm:

npm install worldalphabets

Then, you can use the functions in your project:

const {
  getUppercase,
  getLowercase,
  getFrequency,
  getDigits,
  getAvailableCodes,
  getScripts,
} = require('worldalphabets');

async function main() {
  const codes = await getAvailableCodes();
  console.log('Available codes (first 5):', codes.slice(0, 5));

  const scriptsSr = await getScripts('sr');
  console.log('Serbian scripts:', scriptsSr);

  const uppercaseSr = await getUppercase('sr', scriptsSr[0]);
  console.log('Serbian uppercase:', uppercaseSr);

  const lowercaseFr = await getLowercase('fr');
  console.log('French lowercase:', lowercaseFr);

  const frequencyDe = await getFrequency('de');
  console.log('German frequency for "a":', frequencyDe['a']);

  const digitsAr = await getDigits('ar', 'Arab');
  console.log('Arabic digits:', digitsAr);
}

main();

TypeScript projects receive typings automatically via index.d.ts.

ES Modules (Browser / Node ESM)

If your project uses ES modules (e.g. Vite/webpack/Next.js or "type": "module" in Node), you can import directly. The ES module build also supports automatic candidate selection for language detection.

import { getAvailableCodes, getScripts, detectLanguages } from 'worldalphabets';

const codes = await getAvailableCodes();
console.log('Available codes (first 5):', codes.slice(0, 5));

// Automatic candidate selection (pass null). Also works in the browser.
const textKo = '나는 매우 행복하고 돈이 많이 있습니다.';
const top = await detectLanguages(textKo, null, {}, 3);
console.log(top);
// e.g. [['ko', 0.1203], ['ja', ...], ...]

Notes:

• CommonJS (require) API requires candidateLangs (array) for detection.
• ES Module (import) API supports candidateLangs = null and will select smart candidates via character analysis and embedded word/bigram ranks.
• Browser ESM build ships data with the package and uses static JSON imports — no fs, path, or fetch required.
• The published ESM entry is index.mjs, so bundlers pick it up without extra configuration. index.esm.js remains as a compatibility re-export.

Browser (ESM) usage

Modern bundlers (Webpack 5, Vite, Rollup, Next.js) will automatically pick the browser ESM entry via conditional exports. The browser build is fully static and contains the alphabets, keyboard layouts, and frequency ranks.

// Works in the browser with ESM bundlers
import {
  getIndexData,
  getLanguage,
  getAvailableCodes,
  getScripts,
  detectLanguages,
  getAvailableLayouts,
  loadKeyboard,
  getUnicode,
  extractLayers,
  detectDominantScript,
} from 'worldalphabets';

// Detect language without specifying candidates (short phrases supported)
const res = await detectLanguages('Bonjour comment allez-vous?', null, {}, 3);
console.log(res);

// Use keyboard layouts
const layouts = await getAvailableLayouts();
const kb = await loadKeyboard('fr-french-standard-azerty');
console.log(getUnicode(kb.keys[1], 'base'));

// Inspect modifier layers (Shift, AltGr, etc.)
const layers = extractLayers(kb, ['base', 'shift', 'altgr', 'shift_altgr']);
console.log(layers.shift_altgr.Digit1); // 'À'

// Optional: determine dominant script in an input
console.log(detectDominantScript('Здраво, како си?')); // 'Cyrl'

Notes:

• No network fetches are performed at runtime; data is packaged and statically imported.
• For ambiguous greetings shared by multiple languages, detection may return either (both are acceptable). Use domain context to disambiguate.

Local Usage

If you have cloned the repository, you can use the module directly:

const { getUppercase } = require('./index');

async function main() {
    const uppercaseSr = await getUppercase('sr', 'Latn');
    console.log('Serbian Latin uppercase:', uppercaseSr);
}

main();

Diacritic Utilities

Both interfaces provide helpers to work with diacritic marks.

Python

from worldalphabets import strip_diacritics, has_diacritics

strip_diacritics("café")  # "cafe"
has_diacritics("é")       # True

Node.js

const { stripDiacritics, hasDiacritics } = require('worldalphabets');

stripDiacritics('café'); // 'cafe'
hasDiacritics('é');      // true

Use characters_with_diacritics/charactersWithDiacritics to extract letters with diacritic marks from a list.

Use get_diacritic_variants/getDiacriticVariants to list base letters and their diacritic forms for a given language.

from worldalphabets import get_diacritic_variants

get_diacritic_variants("pl", "Latn")["L"]  # ["L", "Ł"]

const { getDiacriticVariants } = require('worldalphabets');

getDiacriticVariants('pl').then((v) => v.L); // ['L', 'Ł']

Language Detection

The library provides two language detection approaches:

1. Word-based detection (primary): Uses Top-1000 frequency lists for languages with available word frequency data
2. Character-based fallback: For languages without frequency data, analyzes character sets and character frequencies from alphabet data

Automatic Detection (Recommended)

The optimized detection automatically selects candidate languages using character analysis:

from worldalphabets import optimized_detect_languages

# Automatic detection - analyzes ALL 310+ languages intelligently
optimized_detect_languages("Hello world")
# [('en', 0.158), ('de', 0.142), ('fr', 0.139)]

optimized_detect_languages("Аҧсуа бызшәа")  # Abkhazian
# [('ab', 0.146), ('ru', 0.136), ('bg', 0.125)]

optimized_detect_languages("ⲧⲙⲛⲧⲣⲙⲛⲕⲏⲙⲉ")  # Coptic
# [('cop', 0.077)]

# Still supports manual candidate specification
optimized_detect_languages("Hello world", candidate_langs=['en', 'de', 'fr'])
# [('en', 0.158), ('de', 0.142), ('fr', 0.139)]

Manual Candidate Selection

The original detection requires you to specify candidate languages:

from worldalphabets import detect_languages

# Must specify candidate languages
detect_languages("Hello world", candidate_langs=['en', 'de', 'fr'])
# [('en', 0.158), ('de', 0.142), ('fr', 0.139)]

detect_languages("Аҧсуа бызшәа", candidate_langs=['ab', 'ru', 'bg'])
# [('ab', 0.146), ('ru', 0.136), ('bg', 0.125)]

Node.js (Manual Candidates Required)

const { detectLanguages } = require('worldalphabets');

// Must specify candidate languages
detectLanguages('Hello world', ['en', 'de', 'fr']).then(console.log);
// [['en', 0.158], ['de', 0.142], ['fr', 0.139]]

detectLanguages('ⲧⲙⲛⲧⲣⲙⲛⲕⲏⲙⲉ', ['cop', 'el', 'ar']).then(console.log);
// [['cop', 0.077], ['el', 0.032], ['ar', 0.021]]

The detection system supports 310+ languages total: 86 with word frequency data and 224+ with character-based analysis.

Examples

The examples/ directory contains small scripts demonstrating the library:

• examples/python/ holds Python snippets for printing alphabets, collecting stats, listing scripts, and more.
• examples/node/ includes similar examples for Node.js.

Audio Samples

Audio recordings are stored under data/audio/ and named {langcode}_{engine}_{voiceid}.wav. Available voices are listed in data/audio/index.json.

Web Interface

The Vue app under web/ compiles to a static site with npm run build. To work on the interface locally, install its dependencies and start the development server:

cd web
npm install
npm run dev

GitHub Pages publishes the contents of web/dist through a workflow that runs on every push to main.

Each language view is addressable at /<code>, allowing pages to be bookmarked directly.

Alphabet Index

This library also provides an index of all available alphabets with additional metadata.

Python

from worldalphabets import get_index_data, get_language, get_scripts

# Get the entire index
index = get_index_data()
print(f"Index contains {len(index)} languages.")

# Show available scripts for Serbian
scripts = get_scripts("sr")
print(f"Serbian scripts: {scripts}")

# Load Marathi in the Latin script
marathi_latn = get_language("mr", script="Latn")
print(f"Script: {marathi_latn['script']}")
print(f"First letters: {marathi_latn['alphabetical'][:5]}")

Node.js

const { getIndexData, getLanguage, getScripts } = require('worldalphabets');

async function main() {
  // Get the entire index
  const index = await getIndexData();
  console.log(`Index contains ${index.length} languages.`);

  // Show available scripts for Serbian
  const scripts = await getScripts('sr');
  console.log(`Serbian scripts: ${scripts}`);

  // Load Marathi in the Latin script
  const marathiLatn = await getLanguage('mr', 'Latn');
  console.log(`Script: ${marathiLatn.script}`);
  console.log(`First letters: ${marathiLatn.alphabetical.slice(0, 5)}`);
}

main();

Keyboard Layouts

Key entries expose pos (a KeyboardEvent.code when available) along with row, col, and size information.

Python

The script examples/python/keyboard_md_table.py demonstrates rendering a layout as a Markdown table. Copy the layout_to_markdown helper into your project and use it like this:

from keyboard_md_table import layout_to_markdown

print(layout_to_markdown("en-united-kingdom"))

Output:

`	1	2	3	4	5	6	7	8	9	0	-	=
q	w	e	r	t	y	u	i	o	p	[	]
a	s	d	f	g	h	j	k	l	;	'	#
z	x	c	v	b	n	m	,	.	/
␠

or with --offset flag

`	1	2	3	4	5	6	7	8	9	0	-	=
	q	w	e	r	t	y	u	i	o	p	[	]
	a	s	d	f	g	h	j	k	l	;	'	#
		z	x	c	v	b	n	m	,	.	/
					␠

Programmatically, the same data is accessible from Python:

from worldalphabets import load_keyboard, extract_layers

layout = load_keyboard("fr-french-standard-azerty")
layers = extract_layers(layout, ["base", "shift", "altgr", "shift_altgr"])
print(layers["shift_altgr"]["Digit1"])

Node.js

const {
  getAvailableLayouts,
  loadKeyboard,
  getUnicode,
  extractLayers,
} = require('worldalphabets');

async function main() {
  const layouts = await getAvailableLayouts();
  console.log('Available layouts (first 5):', layouts.slice(0, 5));

  const kb = await loadKeyboard('fr-french-standard-azerty');
  console.log('First key Unicode:', getUnicode(kb.keys[1], 'base'));
  const layers = extractLayers(kb, ['base', 'shift', 'altgr', 'shift_altgr']);
  console.log('Shift+AltGr on Digit1:', layers.shift_altgr.Digit1);
}

main();

Supported Languages

For a detailed list of supported languages and their metadata, including available keyboard layouts, see the Alphabet Table.

Developer Guide

Older versions of this project relied on a Java repository and assorted helper scripts to scrape alphabets and estimate letter frequencies. Those utilities have been deprecated in favor of a cleaner pipeline based on Unicode CLDR and Wikidata. The remaining scripts focus on fetching language–script mappings and building alphabet JSON files directly from CLDR exemplar characters, enriching them with frequency counts from the Simia dataset or OpenSubtitles when available.

The alphabet builder preserves the ordering from CLDR exemplar lists and places diacritic forms immediately after their base letters when the CLDR index omits them. For languages with tonal variants such as Vietnamese, common tone marks are stripped before deduplication to avoid generating separate entries for every tone combination.

Each JSON file includes:

• language – English language name
• iso639_3 – ISO 639-3 code
• iso639_1 – ISO 639-1 code when available
• alphabetical – letters of the alphabet (uppercase when the script has case)
• uppercase – uppercase letters
• lowercase – lowercase letters
• frequency – relative frequency of each lowercase letter (zero when no sample text is available)

Example JSON snippet:

{
  "language": "English",
  "iso639_3": "eng",
  "iso639_1": "en",
  "alphabetical": ["A", "B"],
  "uppercase": ["A", "B"],
  "lowercase": ["a", "b"],
  "frequency": {"a": 0.084, "b": 0.0208}
}

Setup

This project uses uv for dependency management. To set up the development environment:

# Install uv
pipx install uv

# Create and activate a virtual environment
uv venv
source .venv/bin/activate

# Install dependencies
uv pip install -e '.[dev]'

Data Generation

Consolidated Pipeline (Recommended)

The WorldAlphabets project uses a unified Python-based data collection pipeline:

# Build complete dataset with all stages
uv run scripts/build_data_pipeline.py

# Build with verbose output
uv run scripts/build_data_pipeline.py --verbose

# Run specific pipeline stage
uv run scripts/build_data_pipeline.py --stage build_alphabets

# Build single language
uv run scripts/build_data_pipeline.py --language mi --script Latn

Pipeline Stages:

1. collect_sources - Download CLDR, ISO 639-3, frequency data
2. build_language_registry - Create comprehensive language database
3. build_alphabets - Generate alphabet files from CLDR + fallbacks
4. build_translations - Add "Hello, how are you?" translations
5. build_keyboards - Generate keyboard layout files
6. build_top1000 - Generate Top-1000 token lists for detection
7. build_tts_index - Index available TTS voices
8. build_audio - Generate audio files using TTS
9. build_index - Create searchable indexes and metadata
10. validate_data - Comprehensive data validation

For detailed pipeline documentation, see docs/DATA_PIPELINE.md.

Legacy Individual Scripts (Deprecated)

The following individual scripts are deprecated in favor of the consolidated pipeline:

Add ISO language codes

uv run scripts/add_iso_codes.py  # Use: --stage build_language_registry

Fetch language-script mappings

uv run scripts/fetch_language_scripts.py  # Use: --stage collect_sources

Build alphabets from CLDR

uv run scripts/build_alphabet_from_cldr.py  # Use: --stage build_alphabets

Generate translations

Populate a sample translation for each alphabet using Google Translate. The script iterates over every language and script combination, writing a hello_how_are_you field to data/alphabets/<code>-<script>.json.

GOOGLE_TRANS_KEY=<key> uv run scripts/generate_translations.py

To skip languages that already have translations:

GOOGLE_TRANS_KEY=<key> uv run scripts/generate_translations.py --skip-existing

Populate keyboard layouts

To refresh keyboard layout references after restructuring, run:

uv run scripts/populate_layouts.py

To skip languages that already have keyboard data:

uv run scripts/populate_layouts.py --skip-existing

Linting and type checking

ruff check .
mypy .

Top-1000 token lists

The language detection helpers rely on comprehensive frequency lists for each language. These lists contain the 1000 most common words per language, sourced from real external data sources like HermitDave FrequencyWords, Leipzig Corpora, and Tatoeba sentences. This provides significantly improved accuracy over the previous top-200 approach.

For major languages (French, English, Spanish), the expanded lists include:

• Common vocabulary: "heureux" (happy), "argent" (money), "travail" (work)
• Numbers, colors, family terms, everyday objects
• Improved word coverage: 60% → 80% for typical sentences

The lists are generated using a unified 5-priority pipeline that maximizes coverage across as many languages as we can:

# Generate expanded frequency lists
uv run python scripts/expand_to_top1000.py

Priority Sources (in order):

1. Leipzig Corpora Collection - High-quality news/web corpora (CC-BY)
2. HermitDave FrequencyWords - OpenSubtitles/Wikipedia sources (CC-BY)
3. Tatoeba sentences - Sentence-based extraction (CC-BY 2.0 FR)
4. Existing alphabet frequency data - Character-level fallback
5. Simia unigrams - CJK character data

The script writes results to data/freq/top1000 with build reports in BUILD_REPORT_UNIFIED.json. The unified pipeline also runs within the consolidated data pipeline as the build_top1000 stage.

Sources

• kalenchukov/Alphabet
• Simia unigrams dataset
• Wikipedia
• ICU locale data
• Unicode
• Kbdlayout

Licence Info

• This project is licensed under the MIT License.
• Data sourced from kalenchukov/Alphabet is licensed under the Apache 2.0 License.
• Data sourced from Simia unigrams dataset (Data from Wiktionary) is licensed under the Creative Commons Attribution-ShareAlike License.
• Data sourced from Wikipedia is licensed under the Creative Commons Attribution-ShareAlike License.