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Add cognate pairs v2 (21.5M pairs) + Phase 8 audit fixes

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Ancient Scripts Datasets — Master Database Reference

Last updated: 2026-03-13 | Commit: 3e3fdf1 | Total entries: 3,466,000+ across 1,178 languages

This document is the single source of truth for understanding, modifying, and extending this database. It is designed for both human researchers and AI agents.

Database Overview
TSV Schema & Format
Ancient Languages — Complete Registry
Non-Ancient Languages — Summary
Source Registry
IPA & Phonetic Processing Pipeline
Transliteration Maps System
Sound Class (SCA) System
Scripts & Data Flow
PRD: Adding New Data
PRD: Adding New Languages
Data Acquisition Rules (Iron Law)
Adversarial Review Protocol
Re-processing & Cleaning Runbook
Known Limitations & Future Work

1. Database Overview

Locations

Location	Path / URL	What
HuggingFace dataset	`https://huggingface.co/datasets/PhaistosLabs/ancient-scripts-datasets`	PRIMARY cloud copy. All lexicons, cognate pairs, metadata, sources, scripts, docs. Push here after any data change.
HuggingFace local clone	`C:\Users\alvin\hf-ancient-scripts\`	Local clone of HuggingFace repo. Use `huggingface_hub` API or `git push` to sync.
GitHub repo	`https://github.com/Nacryos/ancient-scripts-datasets.git`	Scripts, docs, pipeline code. Lexicon data is gitignored but committed via force-add for some ancient langs.
Local working copy	`C:\Users\alvin\ancient-scripts-datasets\`	Full repo + generated data + CLDF sources
CLDF sources	`sources/` (593 MB)	Gitignored. Cloned separately: `northeuralex`, `ids`, `abvd`, `wold`, `sinotibetan`, `wikipron`
Total local footprint	2.2 GB	Includes all generated data + CLDF source repos

What IS Tracked in Git (GitHub)

scripts/ — All extraction and processing scripts
cognate_pipeline/ — Python package for phonetic processing
docs/ — PRDs, audit reports, this reference doc
data/training/metadata/ — languages.tsv, source_stats.tsv (small summary files)
data/training/validation/ — Validation sets (via Git LFS)
data/training/lexicons/*.tsv — Ancient language TSVs (force-added despite gitignore)

What is NOT Tracked in Git (gitignored)

data/training/lexicons/ — Modern language TSVs (1,113 files, regenerated from scripts)
data/training/cognate_pairs/ — Cognate pair datasets (regenerated)
sources/ — CLDF source repositories (cloned separately, ~593 MB)

What IS on HuggingFace (everything)

HuggingFace is the single source of truth for ALL data files. It contains:

All 1,136 lexicon TSVs (ancient + modern)
All cognate pair datasets
All metadata files
All scripts, docs, and pipeline code
All CLDF source repos (2,928 files in sources/)
Raw audit trails and intermediate extraction files

HuggingFace Push Rules

After any data change (new entries, IPA reprocessing, map fixes): push updated TSVs to HF
After any script change that affects output: push scripts to HF

Use huggingface_hub API for individual file uploads:

from huggingface_hub import HfApi
api = HfApi()
api.upload_file(
    path_or_fileobj="data/training/lexicons/ave.tsv",
    path_in_repo="data/training/lexicons/ave.tsv",
    repo_id="PhaistosLabs/ancient-scripts-datasets",
    repo_type="dataset",
    commit_message="fix: reprocess Avestan IPA with expanded transliteration map"
)

For bulk uploads (many files): use upload_large_folder() from the HF local clone at C:\Users\alvin\hf-ancient-scripts\
Always push to BOTH GitHub (scripts/docs) and HuggingFace (data + scripts/docs)
Never let HF fall behind — if data exists locally but not on HF, it's not deployed

To reconstruct all data from scratch:

# 1. Clone CLDF sources
git clone https://github.com/lexibank/northeuralex sources/northeuralex
git clone https://github.com/lexibank/ids sources/ids
git clone https://github.com/lexibank/abvd sources/abvd
git clone https://github.com/lexibank/wold sources/wold
git clone https://github.com/lexibank/sinotibetan sources/sinotibetan
# WikiPron: download from https://github.com/CUNY-CL/wikipron

# 2. Run extraction pipeline
python scripts/expand_cldf_full.py        # Modern languages from CLDF
python scripts/ingest_wikipron.py          # WikiPron IPA data
python scripts/run_lexicon_expansion.py    # Ancient language extraction (requires internet)
python scripts/reprocess_ipa.py            # Apply transliteration maps
python scripts/assemble_lexicons.py        # Generate metadata

Directory Structure

ancient-scripts-datasets/
  data/training/
    lexicons/           # 1,136 TSV files (one per language) [GITIGNORED]
    metadata/           # languages.tsv, source_stats.tsv, etc. [TRACKED]
    cognate_pairs/      # inherited, similarity, borrowing pairs [GITIGNORED]
    validation/         # stratified ML training/test sets [GIT LFS]
    language_profiles/  # per-language markdown profiles
    raw/                # raw JSON audit trails
    audit_trails/       # JSONL provenance logs
  scripts/              # 23 extraction scripts + 7 parsers [TRACKED]
  cognate_pipeline/     # Python package for phonetic processing [TRACKED]
  docs/                 # PRDs, audit reports, this file [TRACKED]
  sources/              # CLDF repos [GITIGNORED, clone separately]

Scale:

1,178 languages (68 ancient/reconstructed + 1,113 modern — 3 overlap)
3,466,000+ total lexical entries
170,756 ancient language entries (68 languages)
3,296,156 modern language entries (1,113 languages)
21,547,916 cognate/borrowing/similarity pairs

Cognate Pairs (v2)

Three TSV files in data/training/cognate_pairs/, 14-column schema:

Lang_A  Word_A  IPA_A  Lang_B  Word_B  IPA_B  Concept_ID  Relationship  Score  Source  Relation_Detail  Donor_Language  Confidence  Source_Record_ID

File	Rows	Description
`cognate_pairs_inherited.tsv`	21,298,208	Expert-classified cognates + concept-aligned pairs (score ≥ 0.5)
`cognate_pairs_borrowing.tsv`	17,924	Verified donor→recipient borrowings from WOLD BorrowingTable
`cognate_pairs_similarity.tsv`	231,784	Phonetically similar pairs (0.3 ≤ score < 0.5), no overlap with inherited

Sources:

ABVD CognateTable (21.6M expert cognate pairs, 1,682 Austronesian languages)
IE-CoR CognateTable (412K Indo-European cognate pairs)
Sino-Tibetan CognateTable (4.2K pairs, borrowings filtered)
WOLD BorrowingTable (17.9K verified donor-recipient pairs)
Internal concept-aligned pairs (233K) + similarity pairs (254K)

Deduplication: Priority ordering expert_cognate > borrowing > concept_aligned > similarity_only. Cross-file dedup ensures no language-concept combo appears in both inherited and similarity files. See docs/prd/PRD_COGNATE_PAIRS_V2.md for full specification.

2. TSV Schema & Format

Every lexicon file follows this 6-column tab-separated schema:

Word	IPA	SCA	Source	Concept_ID	Cognate_Set_ID

Column	Description	Example
Word	Orthographic/transliterated form	`pahhur`, `*wódr̥`, `𐬀𐬵𐬎𐬭𐬀`
IPA	Broad phonemic IPA transcription	`paxːur`, `wodr̩`, `ahura`
SCA	Sound Class Alphabet encoding (18C + 5V)	`PAKUR`, `WOTR`, `AHURA`
Source	Data provenance identifier	`wiktionary`, `ediana`, `wikipron`
Concept_ID	Semantic concept (first gloss word, snake_case)	`fire`, `water`, `-`
Cognate_Set_ID	Cognate grouping identifier	`PIE_fire_001`, `-`

Rules:

Header row MUST be present as line 1
UTF-8 encoding, Unix line endings preferred
No empty IPA fields — use Word as fallback if no conversion possible
Source field must accurately reflect actual data origin
- for unknown/unavailable fields

3. Ancient Languages — Complete Registry

Entry Counts & IPA Quality (as of 2026-03-12)

#	Language	ISO	Family	Entries	Identity%	Top Sources	IPA Type
1	Avestan	ave	Indo-Iranian	3,455	14.4%	avesta_org (2,716), wiktionary_cat (384), wiktionary (355)	Broad phonemic (Hoffmann & Forssman)
2	Tocharian B	txb	Indo-European	2,386	25.2%	wiktionary_cat (2,386)	Broad phonemic (Tocharian map)
3	Luwian	xlw	Anatolian	2,230	26.2%	ediana (1,985), palaeolexicon (225)	Broad phonemic (Luwian map)
4	Proto-Indo-European	ine-pro	Indo-European	1,704	0.2%	wiktionary_cat (863), wiktionary (841)	Broad phonemic (reconstructed)
5	Lycian	xlc	Anatolian	1,098	36.7%	ediana (517), palaeolexicon (482)	Broad phonemic (Melchert 2004)
6	Etruscan	ett	Tyrsenian	753	25.5%	palaeolexicon (503), wikipron (207)	Broad phonemic (Bonfante)
7	Urartian	xur	Hurro-Urartian	748	54.4%	oracc_ecut (704), wiktionary (44)	Partial (cuneiform sign names)
8	Lydian	xld	Anatolian	693	53.0%	ediana (447), palaeolexicon (187)	Broad phonemic (Gusmani 1964)
9	Carian	xcr	Anatolian	532	39.7%	palaeolexicon (304), ediana (174)	Broad phonemic (Adiego 2007)
10	Proto-Kartvelian	ccs-pro	Kartvelian	504	22.2%	wiktionary (254), wiktionary_cat (250)	Broad phonemic (Klimov 1998)
11	Old Persian	peo	Indo-Iranian	486	10.5%	wiktionary (244), wiktionary_cat (242)	Broad phonemic (Kent 1953)
12	Tocharian A	xto	Indo-European	467	23.1%	wiktionary_cat (467)	Broad phonemic (Tocharian map)
13	Proto-Dravidian	dra-pro	Dravidian	406	7.1%	wiktionary_cat (235), wiktionary (171)	Broad phonemic (Krishnamurti)
14	Proto-Semitic	sem-pro	Afroasiatic	386	26.9%	wiktionary_cat (247), wiktionary (139)	Broad phonemic (Huehnergard)
15	Ugaritic	uga	Afroasiatic	371	15.6%	wiktionary (344), wiktionary_cat (27)	Broad phonemic (Tropper 2000)
16	Hittite	hit	Anatolian	266	20.3%	wiktionary (266)	Broad phonemic (Hoffner & Melchert)
17	Hurrian	xhu	Hurro-Urartian	260	50.4%	palaeolexicon (259)	Broad phonemic (Wegner 2007)
18	Elamite	elx	Isolate	301	71.1%	wiktionary (301)	Minimal (transparent orthography)
19	Rhaetic	xrr	Tyrsenian	187	55.1%	tir_raetica (142), wiktionary (45)	Partial (North Italic alphabet)
20	Phoenician	phn	Afroasiatic	180	18.3%	wiktionary (180)	Broad phonemic (abjad reconstruction)
21	Phrygian	xpg	Indo-European	79	36.7%	wiktionary (79)	Partial (small corpus, Greek-script support)
22	Messapic	cms	Indo-European	45	88.9%	wiktionary (45)	Minimal (Greek-alphabet, mostly identity)
23	Lemnian	xle	Tyrsenian	30	53.3%	wiktionary (30)	Minimal (very small corpus)

--- Tier 2 (Phase 6) ---
24	Old English	ang	Germanic	31,319	10.5%	wiktionary_cat (31,319)	Broad phonemic (Hogg 1992)
25	Biblical Hebrew	hbo	Semitic	12,182	0.1%	wiktionary_cat (12,182)	Broad phonemic (Blau 2010)
26	Coptic	cop	Egyptian	11,180	0.1%	wiktionary_cat (7,987), kellia (3,193)	Broad phonemic (Layton 2000)
27	Old Armenian	xcl	Indo-European	6,277	0.0%	wiktionary_cat (6,277)	Broad phonemic (Meillet 1913)
28	Pali	pli	Indo-Aryan	2,792	19.1%	wiktionary_cat (2,792)	Broad phonemic (Geiger 1943)
29	Ge'ez	gez	Semitic	496	0.0%	wiktionary_cat (496)	Broad phonemic (Dillmann 1857)
30	Hattic	xht	Isolate	269	37.9%	wiktionary_cat (269)	Partial (cuneiformist conventions)

--- Tier 3 (Phase 7) ---
31	Old Irish	sga	Celtic	41,300	39.4%	edil (40,309), wiktionary_cat (991)	Broad phonemic (Thurneysen)
32	Old Japanese	ojp	Japonic	5,393	59.7%	oncoj (4,974), wiktionary_cat (419)	Broad phonemic (Frellesvig 2010)
33	Classical Nahuatl	nci	Uto-Aztecan	3,873	5.7%	wiktionary_cat (3,873)	Broad phonemic
34	Oscan	osc	Italic	2,122	15.1%	ceipom (2,122)	Broad phonemic (CEIPoM Standard_aligned)
35	Umbrian	xum	Italic	1,631	3.7%	ceipom (1,631)	Broad phonemic (CEIPoM Standard_aligned)
36	Venetic	xve	Italic	721	86.5%	ceipom (721)	Minimal (Latin transliteration)
37	Gaulish	xtg	Celtic	271	92.3%	diacl (183), wiktionary_cat (88)	Minimal (Latin transliteration)
38	Middle Persian	pal	Indo-Iranian	242	62.8%	wiktionary_cat (242)	Broad phonemic (MacKenzie 1971)
39	Sogdian	sog	Indo-Iranian	194	37.1%	iecor (161), wiktionary_cat (33)	Broad phonemic (Gharib 1995)

--- Proto-Languages (Phase 7) ---
40	Proto-Austronesian	map	Austronesian	11,624	41.1%	acd (11,624)	Broad phonemic (Blust notation)
41	Proto-Germanic	gem-pro	Germanic	5,399	32.9%	wiktionary_cat (5,399)	Broad phonemic (reconstructed)
42	Proto-Celtic	cel-pro	Celtic	1,584	68.3%	wiktionary_cat (1,584)	Partial (mixed Latin/IPA)
43	Proto-Uralic	urj-pro	Uralic	585	50.3%	wiktionary_cat (585)	Broad phonemic (Sammallahti 1988)
44	Proto-Bantu	bnt-pro	Niger-Congo	467	54.0%	wiktionary_cat (467)	Broad phonemic (BLR notation)
45	Proto-Sino-Tibetan	sit-pro	Sino-Tibetan	358	100.0%	wiktionary_cat (358)	Already IPA (Wiktionary provides IPA)

--- Phase 8 Batch 1 (Proto-Languages + Italic/Celtic) ---
46	Proto-Slavic	sla-pro	Balto-Slavic	5,068	18.4%	wiktionary_cat (5,068)	Broad phonemic (reconstructed)
47	Proto-Turkic	trk-pro	Turkic	1,027	27.8%	wiktionary_cat (1,027)	Broad phonemic (reconstructed)
48	Proto-Italic	itc-pro	Italic	739	46.7%	wiktionary_cat (739)	Broad phonemic (reconstructed)
49	Faliscan	xfa	Italic	566	67.1%	ceipom (566)	Partial (CEIPoM Standard_aligned)
50	Proto-Japonic	jpx-pro	Japonic	426	70.2%	wiktionary_cat (426)	Partial (mixed notation)
51	Lepontic	xlp	Celtic	421	27.6%	lexlep (421)	Broad phonemic (Lexicon Leponticum)
52	Proto-Iranian	ira-pro	Indo-Iranian	366	4.6%	wiktionary_cat (366)	Broad phonemic (reconstructed)
53	Ancient South Arabian	xsa	Semitic	127	25.2%	wiktionary (127)	Broad phonemic (Musnad abjad)
54	Celtiberian	xce	Celtic	11	100.0%	wiktionary_cat (11)	Minimal (very small corpus)

--- Phase 8 Batch 2 (Proto-Languages + Ancient) ---
55	Meroitic	xmr	Nilo-Saharan	1,978	39.8%	meroitic-corpus (1,978)	Broad phonemic (Rilly 2007)
56	Proto-Algonquian	alg-pro	Algic	258	28.7%	wiktionary_cat (258)	Broad phonemic (reconstructed)
57	Proto-Albanian	sqj-pro	Albanian	210	43.8%	wiktionary_cat (210)	Broad phonemic (reconstructed)
58	Proto-Austroasiatic	aav-pro	Austroasiatic	180	100.0%	wiktionary_cat (180)	Already IPA (Wiktionary provides IPA)
59	Proto-Polynesian	poz-pol-pro	Austronesian	157	100.0%	wiktionary_cat (157)	Already IPA (Wiktionary provides IPA)
60	Proto-Tai	tai-pro	Kra-Dai	148	0.7%	wiktionary_cat (148)	Broad phonemic (Li 1977)
61	Proto-Tocharian	xto-pro	Tocharian	138	22.5%	wiktionary_cat (138)	Broad phonemic (reconstructed)
62	Proto-Mongolic	xgn-pro	Mongolic	126	41.3%	wiktionary_cat (126)	Broad phonemic (reconstructed)
63	Proto-Oceanic	poz-oce-pro	Austronesian	114	92.1%	wiktionary_cat (114)	Minimal (transparent orthography)
64	Moabite	obm	Semitic	31	0.0%	wiktionary_cat (31)	Broad phonemic (Canaanite abjad)

--- Phase 8 Batch 3 (Proto-Languages + Iberian) ---
65	Proto-Mayan	myn-pro	Mayan	65	20.0%	wiktionary_cat (65)	Broad phonemic (Kaufman 2003)
66	Proto-Afroasiatic	afa-pro	Afroasiatic	48	54.2%	wiktionary_cat (48)	Broad phonemic (Ehret 1995)
67	Iberian	xib	Isolate	39	74.4%	wiktionary_cat (39)	Partial (undeciphered script)

--- Phase 8 Eblaite ---
68	Eblaite	xeb	Semitic	667	0.3%	dcclt-ebla (667)	Broad phonemic (Krebernik 1982)

Total ancient + classical: 170,756 entries across 68 languages | Overall identity rate: ~30%

Understanding Identity Rate

Identity rate = % of entries where Word == IPA (no phonetic conversion applied).

Rate	Meaning	Example Languages
<10%	Excellent IPA conversion	ine-pro (0.2%), dra-pro (7.1%)
10-30%	Good conversion	peo (10.5%), ave (14.4%), hit (20.3%), ccs-pro (22.2%), txb (25.2%)
30-50%	Moderate — some chars unmapped	xlc (36.7%), xcr (39.7%), xhu (50.4%)
50-70%	Partial — significant gaps	xld (53.0%), xur (54.4%), elx (71.1%)
>70%	Minimal — mostly passthrough	cms (88.9%)

Causes of high identity:

Cuneiform sign notation (xur): Uppercase Sumerograms like LUGAL, URU aren't phonemic — 156 entries in xur
Already-IPA characters (cms): Some scripts use characters that ARE IPA (θ, ə, ŋ)
Transparent orthography (elx): Latin letters already map 1:1 to IPA
eDiAna pre-transliterated forms (xlc, xld): Source provides Latin transliterations that are already close to IPA
Plain ASCII stems (txb, xto): Short roots like ak, aik are valid in both orthography and IPA

IPA Quality Categories

Category	Definition	Ancient Languages
FULL	>80% WikiPron-sourced IPA	(none — ancient langs don't have WikiPron)
BROAD PHONEMIC	Scholarly transliteration → IPA via cited map	hit, uga, phn, ave, peo, ine-pro, sem-pro, ccs-pro, dra-pro, xlw, xhu, ett, txb, xto, xld, xcr, xpg
PARTIAL	Some chars converted, gaps remain	xlc, xrr
MINIMAL	Mostly identity / transparent orthography	elx, xle, cms
CUNEIFORM MIXED	Mix of converted transliterations + unconverted sign names	xur

Important: For dead languages, broad phonemic is the ceiling. Narrow allophonic IPA is not possible because allophonic variation is unrecoverable from written records. The IPA column represents the best scholarly reconstruction of phonemic values, not actual pronunciation.

4. Non-Ancient Languages — Summary

1,113 languages with 3,296,156 entries
Dominant source: WikiPron (85.3% of entries = 2,822,808)
Other sources: ABVD (6.7%), NorthEuraLex (5.7%), WOLD (1.8%), sinotibetan (0.1%)

WikiPron entries have true broad phonemic IPA (scraped from Wiktionary pronunciation sections by trained linguists). These are the gold standard.

ABVD entries are often orthographic (Word == IPA). The fix_abvd_ipa.py script applies rule-based G2P conversion for Austronesian languages.

5. Source Registry

Source ID	Full Name	Type	URL	Languages Covered
`wikipron`	WikiPron Pronunciation Dictionary	Scraped IPA	`sources/wikipron/` (local)	800+ modern languages
`abvd`	Austronesian Basic Vocabulary Database	CLDF	`sources/abvd/` (local)	500+ Austronesian
`northeuralex`	NorthEuraLex	CLDF	`sources/northeuralex/` (local)	100+ Eurasian
`wold`	World Loanword Database	CLDF	`sources/wold/` (local)	40+ worldwide
`sinotibetan`	Sino-Tibetan Etymological Database	CLDF	`sources/sinotibetan/` (local)	50+ Sino-Tibetan
`wiktionary`	Wiktionary (appendix/lemma pages)	Web scrape	`en.wiktionary.org`	All ancient langs
`wiktionary_cat`	Wiktionary (category pagination)	MediaWiki API	`en.wiktionary.org/w/api.php`	ine-pro, uga, peo, ave, dra-pro, sem-pro, ccs-pro, txb, xto
`ediana`	eDiAna (LMU Munich)	POST API	`ediana.gwi.uni-muenchen.de`	xlc, xld, xcr, xlw
`palaeolexicon`	Palaeolexicon	REST API	`palaeolexicon.com/api/Search/`	xlc, xld, xcr, xlw, xhu, ett
`oracc_ecut`	Oracc eCUT (Urartian texts)	JSON API	`oracc.museum.upenn.edu/ecut/`	xur
`tir_raetica`	TIR (Thesaurus Inscriptionum Raeticarum)	Web scrape	`tir.univie.ac.at`	xrr
`wikipedia`	Wikipedia vocabulary tables	Web scrape	`en.wikipedia.org`	xur (supplement)
`avesta_org`	Avesta.org Avestan Dictionary	Web scrape	`avesta.org/avdict/avdict.htm`	ave
`kaikki`	Kaikki Wiktionary Dump	JSON dump	`kaikki.org`	Various
`kellia`	Kellia Coptic Lexicon	XML	`data.copticscriptorium.org`	cop
`ceipom`	CEIPoM (Italian Epigraphy)	CSV	`zenodo.org` (CC BY-SA 4.0)	osc, xum, xve
`edil`	eDIL (Electronic Dict of Irish Lang)	XML	`github.com/e-dil/dil`	sga
`acd`	ACD (Austronesian Comparative Dict)	CLDF	`github.com/lexibank/acd` (CC BY 4.0)	map
`oncoj`	ONCOJ (Oxford-NINJAL OJ Corpus)	XML	`github.com/ONCOJ/data` (CC BY 4.0)	ojp
`diacl`	DiACL (Diachronic Atlas of Comp Ling)	CLDF	`github.com/lexibank/diacl` (CC BY 4.0)	xtg
`iecor`	IE-CoR (IE Cognate Relationships)	CLDF	`github.com/lexibank/iecor` (CC BY 4.0)	sog
`lexlep`	Lexicon Leponticum (Zurich)	Web/CSV	`lexlep.univie.ac.at`	xlp
`meroitic-corpus`	Meroitic Language Corpus (GitHub)	JSON/CSV	`github.com/MeroiticLanguage/Meroitic-Corpus`	xmr
`dcclt-ebla`	DCCLT/Ebla (ORACC)	JSON ZIP	`oracc.museum.upenn.edu/dcclt-ebla/` (CC0)	xeb

6. IPA & Phonetic Processing Pipeline

Pipeline Architecture

Source Data (Word column)
    ↓
transliterate(word, iso)          ← scripts/transliteration_maps.py
    ↓                                (greedy longest-match, NFC-normalized)
IPA string (broad phonemic)
    ↓
ipa_to_sound_class(ipa)           ← cognate_pipeline/.../sound_class.py
    ↓                                (tokenize → segment_to_class → join)
SCA string (e.g., "PATA")

IPA Generation Methods (by source type)

Source	IPA Method	Quality
WikiPron	Pre-extracted from Wiktionary pronunciation	True broad IPA
Wiktionary (ancient)	`transliterate(word, iso)` via language-specific map	Broad phonemic
ABVD	Orthographic passthrough → `fix_abvd_ipa.py` G2P	Variable
eDiAna	`transliterate(word, iso)`	Broad phonemic
Palaeolexicon	Source IPA if available, else `transliterate()`	Broad phonemic
Oracc	`transliterate(word, iso)`	Partial (cuneiform)
NorthEuraLex/WOLD	CLDF Segments column → joined IPA	Good

Never-Regress Re-processing Rule

When re-applying transliteration maps to existing data (scripts/reprocess_ipa.py):

candidate_ipa = transliterate(word, iso)

if candidate_ipa != word:
    final_ipa = candidate_ipa       # New map converts — use it
elif old_ipa != word:
    final_ipa = old_ipa             # New map can't, but old was good — keep
else:
    final_ipa = word                # Both identity — nothing to do

This ensures: IPA quality can only improve or stay the same. It never regresses.

7. Transliteration Maps System

File: scripts/transliteration_maps.py (~800 lines)

How It Works

Each ancient language has a Dict[str, str] mapping scholarly transliteration conventions to broad IPA. The transliterate() function applies these via greedy longest-match: keys sorted by descending length, first match consumed at each position.

Map Registry (updated 2026-03-13 — 180+ new rules across 13 original maps + 15 new maps in Phases 6-7 + 24 new maps in Phase 8)

ISO	Language	Keys	Academic Reference
`hit`	Hittite	49	Hoffner & Melchert (2008) — added š, ḫ, macron vowels
`uga`	Ugaritic	68	Tropper (2000) — added ʾ, macron/circumflex vowels, ḫ, ṣ, Ugaritic script (U+10380-1039F)
`phn`	Phoenician	23	Standard 22-letter abjad
`xur`	Urartian	27	Wegner (2007) — added ṣ, ṭ, y, w, ə, ʾ
`elx`	Elamite	19	Grillot-Susini (1987), Stolper (2004)
`xlc`	Lycian	33	Melchert (2004) — added x, j, o, long vowels
`xld`	Lydian	38	Gusmani (1964), Melchert — added ã, ẽ, ũ (nasalized vowels), c, h, z, x
`xcr`	Carian	35	Adiego (2007) — added β, z, v, j, f, ŋ, ĺ, ỳ, ý
`ave`	Avestan	97	Hoffmann & Forssman (1996) + Unicode 5.2 (U+10B00-10B3F)
`peo`	Old Persian	68	Kent (1953) — added z, č, Old Persian cuneiform syllabary (U+103A0-103C3, 31 signs)
`ine`	Proto-Indo-European	61	Fortson (2010), Beekes (2011) — added ḗ, ṓ, morpheme boundaries, accented syllabic sonorants
`sem`	Proto-Semitic	44	Huehnergard (2019)
`ccs`	Proto-Kartvelian	66	Klimov (1998) — added s₁/z₁/c₁/ʒ₁ subscript series, morpheme boundaries
`dra`	Proto-Dravidian	49	Krishnamurti (2003)
`xpg`	Phrygian	55	Brixhe & Lejeune (1984), Obrador-Cursach (2020) — added Greek alphabet support (22 letters)
`xle`	Lemnian	24	Greek-alphabet reconstruction
`xrr`	Rhaetic	26	North Italic alphabet reconstruction
`cms`	Messapic	25	Greek-alphabet reconstruction
`xlw`	Luwian	39	Melchert (2003), Yakubovich (2010)
`xhu`	Hurrian	31	Wegner (2007), Wilhelm (2008)
`ett`	Etruscan	61	Bonfante & Bonfante (2002), Rix (1963) + Old Italic Unicode — added z, o, d, g, b, q, σ→s
`txb`/`xto`	Tocharian A/B	35	Krause & Thomas (1960), Adams (2013), Peyrot (2008) — added retroflex series (ṭ, ḍ, ṇ, ḷ)

--- Phase 6: Tier 2 Maps ---
`cop`	Coptic	40+	Layton (2000), Loprieno (1995) — Sahidic dialect
`pli`	Pali (IAST)	30+	Geiger (1943), Oberlies (2001)
`xcl`	Old Armenian	40+	Meillet (1913), Schmitt (1981)
`ang`	Old English	30+	Hogg (1992), Campbell (1959)
`gez`	Ge'ez (Ethiopic)	50+	Dillmann (1857), Tropper (2002)
`hbo`	Biblical Hebrew	40+	Blau (2010), Khan (2020)

--- Phase 7: Tier 3 + Proto Maps ---
`osc`	Oscan	12	CEIPoM Standard_aligned conventions
`xum`	Umbrian	12	CEIPoM Standard_aligned conventions
`xve`	Venetic	6	CEIPoM Token_clean conventions
`sga`	Old Irish	25	Thurneysen (1946), Stifter (2006) — lenition + macron vowels
`xeb`	Eblaite	20	Standard Semitist notation
`nci`	Classical Nahuatl	15	Andrews (2003), Launey (2011)
`ojp`	Old Japanese	20	Frellesvig (2010), ONCOJ conventions
`pal`	Middle Persian	25	MacKenzie (1971), Skjærvø (2009)
`sog`	Sogdian	25	Gharib (1995), Sims-Williams (2000)
`xtg`	Gaulish	15	Delamarre (2003)
`gem-pro`	Proto-Germanic	20	Ringe (2006), Kroonen (2013)
`cel-pro`	Proto-Celtic	15	Matasović (2009)
`urj-pro`	Proto-Uralic	12	Sammallahti (1988), Janhunen (1981)
`bnt-pro`	Proto-Bantu	20	Bastin et al. (2002), Meeussen (1967)
`sit-pro`	Proto-Sino-Tibetan	18	Matisoff (2003), Sagart (2004)

--- Phase 8 Maps ---
`sla-pro`	Proto-Slavic	25+	Shevelov (1964), Holzer (2007)
`trk-pro`	Proto-Turkic	20+	Clauson (1972), Róna-Tas (1991)
`itc-pro`	Proto-Italic	15+	Meiser (1998), Bakkum (2009)
`jpx-pro`	Proto-Japonic	15+	Vovin (2005), Frellesvig (2010)
`ira-pro`	Proto-Iranian	20+	Cheung (2007), Lubotsky (2001)
`xfa`	Faliscan	12	CEIPoM Standard_aligned conventions
`xlp`	Lepontic	25	Lexicon Leponticum (Stifter et al.)
`xce`	Celtiberian	15+	De Bernardo Stempel (1999)
`xsa`	Ancient South Arabian	30+	Stein (2003), Beeston (1984)
`alg-pro`	Proto-Algonquian	15+	Bloomfield (1946), Goddard (1994)
`sqj-pro`	Proto-Albanian	15+	Orel (1998), Demiraj (1997)
`aav-pro`	Proto-Austroasiatic	10+	Shorto (2006), Sidwell (2015)
`poz-pol-pro`	Proto-Polynesian	10+	Biggs (1978), Pawley (1966)
`tai-pro`	Proto-Tai	20+	Li (1977), Pittayaporn (2009)
`xto-pro`	Proto-Tocharian	15+	Adams (2013), Peyrot (2008)
`poz-oce-pro`	Proto-Oceanic	10+	Ross et al. (1998, 2003, 2008)
`xgn-pro`	Proto-Mongolic	15+	Poppe (1955), Nugteren (2011)
`xmr`	Meroitic	30+	Rilly (2007), Griffith (1911)
`obm`	Moabite	22	Canaanite abjad (shares Phoenician map base)
`myn-pro`	Proto-Mayan	20+	Kaufman (2003), Campbell & Kaufman (1985)
`afa-pro`	Proto-Afroasiatic	15+	Ehret (1995), Orel & Stolbova (1995)
`xib`	Iberian	25+	De Hoz (2010), Untermann (1990)
`xeb`	Eblaite	20+	Krebernik (1982), Fronzaroli (2003)

NFC Normalization

All map keys and input text are NFC-normalized before comparison. This ensures š (U+0161, composed) matches s + combining caron (U+0073 + U+030C, decomposed). Cache is per-ISO to prevent cross-language leakage.

ISO Code Mapping for Proto-Languages

TSV filenames use hyphenated codes but ALL_MAPS uses short codes:

TSV filename ISO	Map ISO
`ine-pro`	`ine`
`sem-pro`	`sem`
`ccs-pro`	`ccs`
`dra-pro`	`dra`
`gem-pro`	`gem-pro`
`cel-pro`	`cel-pro`
`urj-pro`	`urj-pro`
`bnt-pro`	`bnt-pro`
`sit-pro`	`sit-pro`

Adding a New Map

Add the Dict[str, str] constant (e.g., NEW_LANG_MAP) with cited reference
Register in ALL_MAPS: "iso_code": NEW_LANG_MAP
Clear _nfc_cache implicitly (happens on next call with new ISO)
Run reprocess_ipa.py --language iso_code to apply
Deploy adversarial auditor to verify

8. Sound Class (SCA) System

File: cognate_pipeline/src/cognate_pipeline/normalise/sound_class.py

Class Inventory

Class	IPA Segments	Description
A	a, ɑ, æ, ɐ	Open vowels
E	e, ɛ, ə, ɘ, ø, œ	Mid vowels
I	i, ɪ, ɨ	Close front vowels
O	o, ɔ, ɵ	Mid back vowels
U	u, ʊ, ʉ, ɯ, y	Close back vowels
P/B	p, b, ɸ, β	Labial stops
T/D	t, d, ʈ, ɖ	Coronal stops
K/G	k, g, ɡ, q, ɢ	Dorsal stops
S	s, z, ʃ, ʒ, ɕ, ʑ, f, v, θ, ð, x, ɣ, χ, ts, dz, tʃ, dʒ	Fricatives + affricates
M/N	m, n, ɲ, ŋ, ɳ, ɴ	Nasals
L/R	l, ɫ, ɭ, ɬ, r, ɾ, ɽ, ʀ, ɹ, ʁ	Liquids
W/Y	w, ʋ, ɰ, j	Glides
H	ʔ, h, ɦ, ʕ, ħ	Glottals/pharyngeals
0	(anything unmapped)	Unknown

Processing Chain

ipa_to_sound_class("paxːur")
  → tokenize_ipa("paxːur")  →  ["p", "a", "xː", "u", "r"]
  → [segment_to_class(s) for s in segments]  →  ["P", "A", "K", "U", "R"]
  → "PAKUR"

9. Scripts & Data Flow

Data Flow Diagram

EXTERNAL SOURCES
  ├── Wiktionary API ──────────→ extract_ave_peo_xpg.py
  │                              extract_phn_elx.py
  │                              extract_pie_urartian.py
  │                              extract_wiktionary_lexicons.py
  │                              expand_wiktionary_categories.py
  │                              expand_xpg.py
  ├── eDiAna API ──────────────→ scrape_ediana.py
  ├── Palaeolexicon API ───────→ scrape_palaeolexicon.py
  ├── Oracc JSON API ──────────→ scrape_oracc_urartian.py
  ├── avesta.org ──────────────→ scrape_avesta_org.py
  ├── TIR (Vienna) ────────────→ scrape_tir_rhaetic.py
  ├── WikiPron TSVs ───────────→ ingest_wikipron.py
  └── CLDF Sources ────────────→ expand_cldf_full.py
                                  convert_cldf_to_tsv.py
                     ↓
         data/training/lexicons/{iso}.tsv
                     ↓
         normalize_lexicons.py (NFC, dedup, strip stress)
         reprocess_ipa.py (re-apply updated transliteration maps)
         fix_abvd_ipa.py (Austronesian G2P fix)
                     ↓
         assemble_lexicons.py → metadata/languages.tsv
         assign_cognate_links.py → cognate_pairs/*.tsv
         build_validation_sets.py → validation/*.tsv

Script Quick Reference

Script	Purpose	Languages
`extract_ave_peo_xpg.py`	Wiktionary Swadesh + category	ave, peo, xpg
`extract_phn_elx.py`	Wiktionary + appendix	phn, elx
`extract_pie_urartian.py`	Wiktionary + Wikipedia	ine-pro, xur
`extract_wiktionary_lexicons.py`	Wiktionary appendix	sem-pro, ccs-pro, dra-pro, xle
`extract_anatolian_lexicons.py`	Multi-source	xlc, xld, xcr
`expand_wiktionary_categories.py`	Wiktionary category pagination	ine-pro, uga, peo, ave, dra-pro, sem-pro, ccs-pro
`expand_xpg.py`	Wiktionary category + appendix	xpg
`scrape_ediana.py`	eDiAna POST API	xlc, xld, xcr, xlw
`scrape_palaeolexicon.py`	Palaeolexicon REST API	xlc, xld, xcr, xlw, xhu, ett
`scrape_avesta.py`	avesta.org (old, superseded)	ave
`scrape_avesta_org.py`	avesta.org dictionary (current, adversarial-audited)	ave
`scrape_oracc_urartian.py`	Oracc eCUT JSON API	xur
`scrape_tir_rhaetic.py`	TIR web scrape	xrr
`ingest_wikipron.py`	WikiPron TSV ingestion	800+ modern
`expand_cldf_full.py`	CLDF full extraction	All CLDF languages
`reprocess_ipa.py`	Re-apply transliteration maps	23 ancient
`fix_abvd_ipa.py`	G2P for Austronesian	ABVD languages
`normalize_lexicons.py`	NFC + dedup + SCA recompute	All
`assemble_lexicons.py`	Generate metadata	All
`ingest_wiktionary_tier2.py`	Wiktionary category ingestion (Tier 2+)	Phase 6-8 Wiktionary languages
`fetch_wiktionary_raw.py`	Fetch raw Wiktionary category JSON	Phase 6-8 Wiktionary languages
`ingest_dcclt_ebla.py`	ORACC DCCLT/Ebla extraction	xeb
`ingest_meroitic.py`	Meroitic Language Corpus	xmr
`ingest_lexlep.py`	Lexicon Leponticum extraction	xlp
`ingest_ceipom_italic.py`	CEIPoM italic epigraphy	osc, xum, xve, xfa
`update_metadata.py`	Update languages.tsv from disk	All
`validate_all.py`	Comprehensive TSV validation	All
`push_to_hf.py`	Push files to HuggingFace	All Phase 6-8

10. PRD: Adding New Data to Existing Languages

Prerequisites

The language already has a TSV file in data/training/lexicons/
You have identified a new external source with verifiable data
A transliteration map exists in transliteration_maps.py (if ancient)

Step-by-Step

Step 1: Identify Source

Find a publicly accessible online source (API, web page, database)
Verify it returns real lexical data (not AI-generated)
Document the URL, API format, and expected entry count

Step 2: Write Extraction Script

# Template: scripts/scrape_{source}_{iso}.py
#!/usr/bin/env python3
"""Scrape {Source Name} for {Language} word lists.
Source: {URL}
"""
import urllib.request  # MANDATORY — proves data comes from HTTP
...

def fetch_data(url):
    """Fetch from external source."""
    req = urllib.request.Request(url, headers={"User-Agent": "..."})
    with urllib.request.urlopen(req) as resp:
        return json.loads(resp.read())

def process_language(iso, config, dry_run=False):
    """Process and deduplicate."""
    existing = load_existing_words(tsv_path)  # MUST deduplicate
    entries = fetch_data(url)
    new_entries = [e for e in entries if e["word"] not in existing]
    ...
    # Apply transliteration
    ipa = transliterate(word, iso)
    sca = ipa_to_sound_class(ipa)
    f.write(f"{word}\t{ipa}\t{sca}\t{source_id}\t{concept_id}\t-\n")

Critical: Script MUST contain urllib.request.urlopen(), requests.get(), or equivalent HTTP fetch. No hardcoded word lists.

Step 3: Run with --dry-run

python scripts/scrape_new_source.py --dry-run --language {iso}

Step 4: Run Live

python scripts/scrape_new_source.py --language {iso}

Step 5: Re-process IPA (if map was updated)

python scripts/reprocess_ipa.py --language {iso}

Step 6: Deploy Adversarial Auditor

See Section 13.

Step 7: Commit & Push to Both Repos

# GitHub
git add scripts/scrape_new_source.py data/training/lexicons/{iso}.tsv
git commit -m "Add {N} entries to {Language} from {Source}"
git push

# HuggingFace (MANDATORY — HF is the primary data host)
python -c "
from huggingface_hub import HfApi
api = HfApi()
for f in ['data/training/lexicons/{iso}.tsv', 'scripts/scrape_new_source.py']:
    api.upload_file(path_or_fileobj=f, path_in_repo=f,
                    repo_id='PhaistosLabs/ancient-scripts-datasets', repo_type='dataset',
                    commit_message='Add {N} entries to {Language} from {Source}')
"

11. PRD: Adding New Languages

Prerequisites

ISO 639-3 code identified
At least one external source with verifiable word lists
Script conventions for the relevant writing system understood

Step-by-Step

Step 1: Create Transliteration Map (if needed)

Add to scripts/transliteration_maps.py:

# ---------------------------------------------------------------------------
# N. NEW_LANGUAGE  (Author Year, "Title")
# ---------------------------------------------------------------------------
NEW_LANGUAGE_MAP: Dict[str, str] = {
    "a": "a", "b": "b", ...
    # Every key MUST have a cited academic reference
}

ALL_MAPS = {
    ...
    "new_iso": NEW_LANGUAGE_MAP,
}

Step 2: Write Extraction Script

Follow the template in Section 10. The script must:

Fetch from an external source via HTTP
Parse the response (HTML, JSON, XML)
Apply transliterate() and ipa_to_sound_class()
Write to data/training/lexicons/{iso}.tsv
Save raw JSON to data/training/raw/ for audit trail
Deduplicate by Word column

Step 3: Add to Language Config (optional)

If the language will be part of the ancient languages pipeline, add to scripts/language_configs.py.

Step 4: Add to Re-processing List

Add the ISO code to ANCIENT_LANGUAGES in scripts/reprocess_ipa.py and to ISO_TO_MAP_ISO if the TSV filename differs from the map ISO.

Step 5: Run Extraction

python scripts/scrape_{source}.py --language {iso} --dry-run
python scripts/scrape_{source}.py --language {iso}

Step 6: Verify

# Check entry count and IPA quality
python scripts/reprocess_ipa.py --dry-run --language {iso}

Step 7: Deploy Adversarial Auditor

See Section 13.

Step 8: Commit and Push

12. Data Acquisition Rules (Iron Law)

┌─────────────────────────────────────────────────────────────────────┐
│  DATA MAY ONLY ENTER THE DATASET THROUGH CODE THAT DOWNLOADS IT    │
│  FROM AN EXTERNAL SOURCE.                                          │
│                                                                     │
│  NO EXCEPTIONS. NO "JUST THIS ONCE." NO "IT'S FASTER."             │
└─────────────────────────────────────────────────────────────────────┘

What IS Allowed

Action	Example	Why OK
Write a script with `urllib.request.urlopen()`	`scrape_palaeolexicon.py`	Data comes from HTTP
Parse HTML/JSON from downloaded content	`BeautifulSoup(html)`	Deterministic extraction
Apply transliteration map (CODE, not DATA)	`transliterate(word, "hit")`	Transformation rules are code
Re-compute SCA from IPA	`ipa_to_sound_class(ipa)`	Deterministic function

What is FORBIDDEN

Action	Example	Why Forbidden
Write data rows directly	`f.write("water\twɔːtər\t...")`	Data authoring
Hardcode word lists from memory	`WORDS = [("fire", "paxːur")]`	LLM knowledge ≠ source
Fill in missing fields with guesses	`ipa = "probably θ"`	Hallucination risk
Generate translations/transcriptions	`ipa = "wɔːtər" # I know how water sounds`	Not from a source
Pad entries to reach a target count	Adding 13 entries to make it 200	Fabrication

The Cached-Fetch Pattern (Acceptable Gray Area)

If a source requires JavaScript rendering or CAPTCHAs:

Use WebFetch/browser to access the source
Save raw content to data/training/raw/{source}_{iso}_{date}.html
Write a parsing script that reads from the saved file
The auditor spot-checks 5 entries against the live source

Transliteration Maps Are CODE, Not DATA

Transliteration maps (e.g., "š": "ʃ") are transformation rules derived from published grammars, not lexical content. Adding or modifying map entries is a code change, not data authoring. However, every map entry MUST cite an academic reference.

13. Adversarial Review Protocol

Architecture: Dual-Agent System

Team A (Extraction Agent)     Team B (Adversarial Auditor)
  ├── Writes code               ├── Reviews code
  ├── Runs scripts              ├── Spot-checks output
  ├── Produces TSV data         ├── Verifies provenance
  └── NEVER writes data         └── Has VETO POWER
       directly

When to Deploy

After ANY new data is added to the database
After ANY transliteration map change
After ANY re-processing run
After ANY script modification that affects output

Audit Checklist (per modular step)

Code Review

Script contains urllib/requests/curl (not hardcoded data)
No literal IPA data in f.write() calls
Source attribution matches actual source
Deduplication against existing entries

Data Quality

Entry count is non-round and plausible
No duplicate Word values
No empty IPA fields
Identity rate is explainable (not suspiciously low or high)
SCA matches ipa_to_sound_class(IPA) for 20 random samples

Never-Regress Verification

No entry went from non-identity IPA to identity (regression)
Entry counts did not decrease
Existing Word/Source/Concept_ID/Cognate_Set_ID unchanged

Provenance

20 random entries traced back to source URL
Raw JSON/HTML audit trail saved in data/training/raw/

Red Flags (STOP immediately)

Red Flag	What It Means
No `urllib`/`requests` in extraction code	Agent is authoring data
Entry count is exactly round (100, 200, 500)	Likely padded
>90% of entries have empty required fields	Extraction didn't work
Script contains `f.write("word\tipa\t...")` with literal data	Direct data authoring
Transformation output == input for >80% without cited justification	Map not actually applied

Report Format

# Adversarial Audit: {Step} — {Language} ({iso})
## Checks:
- [ ] No data authoring: PASS/FAIL
- [ ] Entry count: PASS/FAIL (expected X, got Y)
- [ ] IPA quality: PASS/FAIL (identity rate: Z%)
- [ ] SCA consistency: PASS/FAIL (N/N verified)
- [ ] Provenance: PASS/FAIL (N/20 traced to source)
## Verdict: PASS / WARN / FAIL
## Blocking: YES (if FAIL)

14. Re-processing & Cleaning Runbook

When to Re-process

After modifying any transliteration map in transliteration_maps.py
After fixing a bug in transliterate() or ipa_to_sound_class()
After adding a new language to ALL_MAPS

How to Re-process

# Dry run first (ALWAYS)
python scripts/reprocess_ipa.py --dry-run

# Check: identity rates should decrease or stay the same, NEVER increase
# Check: "Changed" column shows expected number of modifications
# Check: "Errors" column is 0

# Run live
python scripts/reprocess_ipa.py

# Or for a single language
python scripts/reprocess_ipa.py --language xlw

Common Cleaning Operations

Remove entries with HTML artifacts

# Check for HTML entities
grep -P '&\w+;' data/training/lexicons/{iso}.tsv
# Remove affected lines via Python script (not manual edit)

Remove entries from wrong source (contamination)

# Example: Hurrian TSV had Hittite entries from wrong Palaeolexicon ID
# Write a Python script that identifies and removes contaminated entries
# Save removed entries to audit trail

Deduplicate

# reprocess_ipa.py handles dedup by Word column
# For more complex dedup, use normalize_lexicons.py

Fix ABVD fake-IPA

python scripts/fix_abvd_ipa.py

Post-Cleaning Verification

# Verify entry counts
python -c "
for iso in ['hit','uga',...]:
    with open(f'data/training/lexicons/{iso}.tsv') as f:
        print(f'{iso}: {sum(1 for _ in f) - 1} entries')
"

# Verify no empty IPA
python -c "
for iso in [...]:
    with open(f'data/training/lexicons/{iso}.tsv') as f:
        for line in f:
            parts = line.strip().split('\t')
            if len(parts) >= 2 and not parts[1]:
                print(f'EMPTY IPA: {iso} {parts[0]}')
"

15. Known Limitations & Future Work

Linguistic Limitations

Issue	Languages Affected	Root Cause
Broad phonemic only (no allophonic)	All ancient	Dead languages — allophonic variation unrecoverable
Cuneiform sign names as entries	xur, xhu	Source provides sign-level notation, not phonemic. ~156 Sumerograms in xur.
High identity for transparent orthographies	elx, cms, xle	Writing system maps 1:1 to IPA
Old Persian ç → θ debatable	peo	Kent (1953) says /θ/, Kloekhorst (2008) says /ts/
Old Persian cuneiform inherent vowels	peo	Syllabary signs (𐎣=ka, 𐎫=ta) include inherent vowels that may be redundant in context
eDiAna entries drive high identity	xlc, xld	eDiAna provides already-transliterated forms; identity is expected, not a map gap

Technical Debt

Issue	Priority	Fix
`use_word_for_ipa` dead config in expand_wiktionary_categories.py	Low	Remove the config key
Some extraction scripts have hardcoded word lists from pre-Iron-Law era	Medium	Rewrite with HTTP fetch
ABVD entries still ~50% fake-IPA after G2P fix	Medium	Better G2P or manual review
NorthEuraLex/WOLD join segments with spaces	Low	Handled by normalize_lexicons.py
Combining diacritics in Lycian/Carian (U+0303, U+0302)	Low	Normalize in preprocessing before transliteration
Greek letter leaks in Carian source data	Low	Data cleaning script to normalize σ→s, α→a, etc.
HTML entities in 4 PIE IPA entries	Low	Decode with `html.unescape()` in reprocess_ipa.py
15 Old Persian proper nouns have wrong-language IPA	Low	Filter or manually correct Akkadian/Greek transcriptions

Expansion Opportunities

Language	Current	Available	Source
Sumerian	0	5,000+	EPSD2 (ePSD), Oracc
Akkadian	0	10,000+	CAD, CDA, ePSD2
Egyptian	0	3,000+	TLA (Thesaurus Linguae Aegyptiae)
Sanskrit	(modern only)	50,000+	Monier-Williams, DCS
Linear B	0	500+	DAMOS, Wingspread
Luvian Hieroglyphic	(mixed with xlw)	500+	Hawkins (2000)

Appendix A: Quick Commands

# Count entries for a language
wc -l data/training/lexicons/{iso}.tsv

# Check identity rate
python -c "
with open('data/training/lexicons/{iso}.tsv') as f:
    lines = f.readlines()[1:]
    total = len(lines)
    identity = sum(1 for l in lines if l.split('\t')[0] == l.split('\t')[1])
    print(f'{identity}/{total} = {identity/total*100:.1f}%')
"

# Test a transliteration map
python -c "
import sys; sys.path.insert(0, 'scripts')
from transliteration_maps import transliterate
print(transliterate('test_word', 'iso_code'))
"

# Re-process single language (dry run)
python scripts/reprocess_ipa.py --dry-run --language {iso}

# Run adversarial audit (deploy via AI agent)
# See Section 13 for protocol

Appendix B: File Checksums Reference

Run after any batch operation to create a baseline:

find data/training/lexicons -name "*.tsv" -exec wc -l {} \; | sort -k2 > /tmp/lexicon_counts.txt

Ancient Scripts Datasets — Master Database Reference

Table of Contents

1. Database Overview

Locations

What IS Tracked in Git (GitHub)

What is NOT Tracked in Git (gitignored)

What IS on HuggingFace (everything)

HuggingFace Push Rules

Directory Structure

Cognate Pairs (v2)

2. TSV Schema & Format

3. Ancient Languages — Complete Registry

Entry Counts & IPA Quality (as of 2026-03-12)

Understanding Identity Rate

IPA Quality Categories

4. Non-Ancient Languages — Summary

5. Source Registry

6. IPA & Phonetic Processing Pipeline

Pipeline Architecture

IPA Generation Methods (by source type)

Never-Regress Re-processing Rule

7. Transliteration Maps System

How It Works

Map Registry (updated 2026-03-13 — 180+ new rules across 13 original maps + 15 new maps in Phases 6-7 + 24 new maps in Phase 8)

NFC Normalization

ISO Code Mapping for Proto-Languages

Adding a New Map

8. Sound Class (SCA) System

Class Inventory

Processing Chain

9. Scripts & Data Flow

Data Flow Diagram

Script Quick Reference

10. PRD: Adding New Data to Existing Languages

Prerequisites

Step-by-Step

Step 1: Identify Source

Step 2: Write Extraction Script

Step 3: Run with --dry-run

Step 4: Run Live

Step 5: Re-process IPA (if map was updated)

Step 6: Deploy Adversarial Auditor

Step 7: Commit & Push to Both Repos

11. PRD: Adding New Languages

Prerequisites

Step-by-Step

Step 1: Create Transliteration Map (if needed)

Step 2: Write Extraction Script

Step 3: Add to Language Config (optional)

Step 4: Add to Re-processing List

Step 5: Run Extraction

Step 6: Verify

Step 7: Deploy Adversarial Auditor

Step 8: Commit and Push

12. Data Acquisition Rules (Iron Law)

What IS Allowed

What is FORBIDDEN

The Cached-Fetch Pattern (Acceptable Gray Area)

Transliteration Maps Are CODE, Not DATA

13. Adversarial Review Protocol

Architecture: Dual-Agent System

When to Deploy

Audit Checklist (per modular step)

Code Review

Data Quality

Never-Regress Verification

Provenance

Red Flags (STOP immediately)

Report Format

14. Re-processing & Cleaning Runbook

When to Re-process

How to Re-process

Common Cleaning Operations

Remove entries with HTML artifacts

Remove entries from wrong source (contamination)

Deduplicate

Fix ABVD fake-IPA

Post-Cleaning Verification

15. Known Limitations & Future Work

Linguistic Limitations