Global Language Family Mapper (GLFM)

A modular, JSON-based language database and validation pipeline for over 7,900 languages (ISO 639-3), including Uralic/Finno-Ugric languages. Designed for RSS feeds, localization, and NLP workflows.

• Author: Tuomas Lähteenmäki
• License: MIT
• Version: 1.0.6

---

---

Primary Links

• Canonical Repository (Codeberg): https://codeberg.org/lahtis/GLFM
• GitHub Mirror: https://github.com/lahtis/GLFM

---

Features

• 7,900+ Languages: Unified from ISO 639-1/2/3/5, Wikidata (Glottolog), CLDR, and POS statistics.
• Nearest Language Neighbors: Each written language includes typologically nearest languages based on URIEL/lang2vec features (syntax, phonology, inventory, family).
• Accurate BCP-47 Tags: Language, script, and region combined automatically.
• Fallback Chains: Automatically assigns fallback languages where applicable.
• Uralic/Finno-Ugric Support: Detailed mapping for Finnish, Karelian, Estonian, and related languages.
• Validation Pipeline: Full automated checks for ISO consistency, BCP-47 compliance, fallback correctness, POS stats, and Glottolog data.
• Checkpoint/Resume Support: Long-running nearest-language computations can be resumed automatically from a checkpoint.

---

Directory Structure

GLFM/
├── pyproject.toml
├── config.toml                    # Default configuration
├── README.md
├── LICENCE
├── output/                        # Pipeline outputs (.gitignore)
│   ├── unified/                   # unified_languages.json, languages_top20.json.gz
│   ├── validation/                # validation_report.md/html, errors
│   └── logs/
├── src/
│   └── glfm/
│       ├── __init__.py
│       ├── _version.py
│       ├── paths.py               # Paths (cache, data, output)
│       ├── config.py              # Configuration
│       ├── download.py            # Data downloading
│       ├── build_unified.py       # Unified database builder
│       ├── run_full_pipeline.py   # Full pipeline
│       ├── data/                  # Internal data (packaged)
│       │   ├── override_scripts.json
│       │   └── uralic_languages.json
│       ├── loaders/               # Data source loaders
│       ├── logic/                 # Logic (script, region, fallback, BCP-47)
│       ├── models/                # Data models (Language)
│       └── tools/                 # Validators, reports, helper scripts
└── venv/

Note: Downloadable data (iso_639_*.py, glottolog.json, cldr/) lives in the cache (~/.cache/glfm/data/), not in the repo. Bundled data (override_scripts.json, uralic_languages.json) is shipped with the package (src/glfm/data/).

---

Data Structure (unified_languages.json)

Each language is represented as a JSON object keyed by its ISO 639-3 code:

"fin": {
  "bcp47": "fi-Latn-FI",
  "default_region": "FI",
  "default_script": "Latn",
  "distance_bases":  [
    "WALS",
    "SSWL",
    "PHOIBLE",
    "Ethnologue",
    "Glottolog"
   ],
  "distance_source": "lang2vec / URIEL",
  "distance_type": "fam",
  "fallback": "fin",
  "family": "Finnic",
  "glottocode": "finn1318",
  "glottolog": {
    "glottocode": "finn1318",
    "iso639_3": "fin",
    "family": "Finnic",
    "macroarea": "",
    "latitude": 62.0,
    "longitude": 25.0,
    "lineage":  [ ],
    "status": ""
  },
  "id": "fin",
  "iso639_1": "fi",
  "iso639_2B": "fin",
  "iso639_2T": "fin",
  "iso639_3": "fin",
  "iso639_5": "",
  "language_type": "L",
  "name": "Finnish",
  "nearest_languages":  [
    {
      "lang": "est",
      "distance": 0.0003
    },
    {
      "lang": "izh",
      "distance": 0.0003
    }
   ],
  "official_name": "Finnish",
  "pos _stats": {},
  "uralicNLP": true,
  "written": true,
  "written_scripts":  [
    "Latn"
   ]
}

• bcp47 is automatically generated: lang[-Script][-Region].
• fallback is the default fallback language.
• uralicNLP is true if the language belongs to the Uralic/Finno-Ugric family.
• written_scripts lists all known writing scripts.
• nearest_languages ranks typologically nearest written languages by distance (up to 100 max_neighbors).
• language_type is SIL Language Type (L = Living, E = Extinct, H = Historical, A = Ancient, C = Constructed).
• family is Glottolog family name (e.g. "Finnic"), not an ISO 639-5 code.
• glottocode is Glottolog language code (e.g. "finn1318").

---

Pipeline Overview

The pipeline is fully automated:

generate ISO → build glottolog → build unified → compute nearest → build consumer → validate → report → visualize

Step	Module	Description
generate	glfm.tools.generate_iso_files	Download ISO 639 sources
glottolog	glfm.tools.build_glottolog_json	Fetch Glottolog metadata (Wikidata)
build	glfm.build_unified	Build unified database
compute	glfm.tools.compute_nearest_languages	Compute neighbors (lang2vec)
consumer	glfm.tools.build_consumer_dataset	Build consumer dataset (top-20)
bcp47	glfm.tools.validate_bcp47	Validate BCP-47
fallbacks	glfm.tools.validate_fallbacks	Validate fallbacks
iso	glfm.tools.validate_iso_consistency	Validate ISO codes
pos	glfm.tools.validate_pos_stats	Validate POS statistics
glotto	glfm.tools.validate_glottolog	Validate Glottolog
report	glfm.tools.generate_report	Generate reports
viz	glfm.tools.visualize_fallbacks	Visualize fallbacks (optional)

---

CLI

Full pipeline

python3 -m glfm.run_full_pipeline

Skip heavy neighbor computation

python3 -m glfm.run_full_pipeline --skip compute

Run only selected steps

python3 -m glfm.run_full_pipeline --only build bcp47

List steps

python3 -m glfm.run_full_pipeline --list

---

Configuration

config.toml (repo root) contains default values. Users can override:

• ~/.config/glfm/config.toml
• Environment variables: GLFM_DATA_DIR, GLFM_CACHE_DIR, GLFM_OUTPUT_DIR, GLFM_ISO_639_3_URL, etc.

Example:

[sources.iso_639_3]

url = "https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab"

[sources.cldr]

version  = "48.0.0"

[sources.wikidata]

endpoint = "https://query.wikidata.org/sparql"

[paths]

data_dir = ""        # empty  =  ~/.cache/glfm/data

cache_dir = ""       # empty  =  ~/.cache/glfm

---

Quick Start

1 . Install

pip install glfm

2. Get data

Choose one of these:

Option A: Download pre-built data (recommended)

python3 -m glfm.download                    # consumer (558 kB)
python3 -m glfm.download --tier research    # research (1.4 MB)

Option B: Generate data from sources (slow, requires network)

python3 -m glfm.run_full_pipeline --only generate glottolog

3 . Run the full pipeline

python3 -m glfm.run_full_pipeline

4 . Access data in Python

import gzip, json
from pathlib import Path

cache = Path.home() / ".cache" / "glfm" / "data"
with gzip.open(cache / "unified_languages.json.gz", "rt") as f:
    languages = json.load(f)

fin = languages["fin"]
print(fin["bcp47"])                  # fi-Latn-FI
print(fin["family"])                 # Finnic
print(fin["glottocode"])             # finn1318
print(fin["nearest_languages"][:5])  # Top 5 nearest languages

4 . Consumer dataset (top-20 neighbors)

import gzip
import json
from pathlib import Path

# Option A: read from cache (after `glfm.download`)
cache = Path.home() / ".cache" / "glfm" / "data"
# Option B: read from pipeline output (after `glfm.run_full_pipeline`)
# output = Path("output/unified")

with gzip.open(cache / "languages_top20.json.gz", "rt") as f:
    languages = json.load(f)

fin = languages["fin"]
print(f"bcp47: {fin['bcp47']}")           # fi-Latn-FI
print(f"family: {fin['family']}")         # Finnic
print(f"glottocode: {fin['glottocode']}") # finn1318
print(f"language_type: {fin['language_type']}")  # L
print(f"neighbors: {len(fin['nearest_languages'])}")  # 20

for nb in fin["nearest_languages"]:
    print(f"{nb['lang']}: {nb['distance']:.4f}")

5. Research dataset (up to 100 neighbors)

import gzip
import json
from pathlib import Path

# Option A: read from cache (after `glfm.download --tier research`)
cache = Path.home() / ".cache" / "glfm" / "data"
# Option B: read from pipeline output (after `glfm.run_full_pipeline`)
# output = Path("output/unified")

with gzip.open(cache / "unified_languages.json.gz", "rt") as f:
    languages = json.load(f)

fin = languages["fin"]
print(f"neighbors: {len(fin['nearest_languages'])}")  # 100

for nb in fin["nearest_languages"]:
    print(f"{nb['lang']}: {nb['distance']:.4f}")

---

Requirements

• Python 3.9+
• Core dependencies: none (pure stdlib)
• Dev dependencies: numpy, requests, msgpack, lang2vec, graphviz, pytest

Installation

1. Create virtual environment

git clone https://codeberg.org/lahtis/GLFM.git
cd GLFM
python3 -m venv venv
source venv/bin/activate    # Linux/macOS
# venv\Scripts\activate     # Windows

2. Install package

pip install glfm

3. Install development dependencies (optional)

pip install -e ".[dev]"

or

git clone https://codeberg.org/lahtis/GLFM.git
cd GLFM
python3 -m venv venv
source venv/bin/activate
pip install -e .

---

Data Sources & Acknowledgments

• Glottolog: Hammarström, Harald & Forkel, Robert & Haspelmath, Martin & Bank, Sebastian. Glottolog. (glottolog.org) – fetched via Wikidata SPARQL.
• URIEL / lang2vec: Typological vector representations.
• ISO 639: Language code standards (ISO 639-1, 639-2, 639-3, 639-5).
• CLDR / Wiktionary: Locale display names, scripts.

---

Contributing & Issues

https://codeberg.org/lahtis/GLFM

---

License

Copyright © 2026 Tuomas Lähteenmäki
MIT License. See LICENSE for details.

---

Key changes

#	Section	Change
1	Python version	3.8+ → 3.9+
2	Directory Structure	src/glfm/ layout
3	Data Structure	family: "Finnic", glottocode: "finn1318", language_type: "L"
4	nearest_languages	5 → 100 (max)
5	Pipeline Overview	12 steps (incl. glottolog, consumer)
6	CLI	python3 -m glfm.run_full_pipeline
7	Configuration	config.toml + env vars
8	Requirements	No core dependencies (pure stdlib)
9	build_top5.py	build_consumer_dataset.py
10	"Glottolog future work"	Glottolog works (Wikidata)