Metadata-Version: 2.4
Name: thaiphon
Version: 0.4.0
Summary: Thai phonology engine with pluggable transliteration systems.
Author: Boris Savinov
License:                                  Apache License
                                   Version 2.0, January 2004
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License-File: LICENSE
License-File: NOTICE
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
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Description-Content-Type: text/markdown

# thaiphon

A zero-dependency Thai phonological transliteration engine. 75 % exact-match
accuracy against independent Wiktionary IPA ground truth when installed with
the optional `thaiphon-data-volubilis` lexicon package; 57 % on the base
engine alone (see [Accuracy](#accuracy) for details).

- **Try it online**: [rianthai.pro/thai-transliteration](https://rianthai.pro/thai-transliteration/) — no install needed. Built for teachers, Thai language learners, and anyone who just wants to type a word and see its pronunciation.
- **Documentation**: [rianthai.pro/docs/thaiphon](https://rianthai.pro/docs/thaiphon/)

## What it does

Thai script goes in; transliteration comes out. The engine first resolves
the input into a phonological word — a tuple of syllables, each
decomposed into IPA-based onset, vowel quality, vowel length, coda, and
tone. Every output scheme is then a small declarative `SchemeMapping`
that renames those phonemes into a target surface form. Add a new
scheme by writing data, not code.

Three built-in renderers ship with the package:

- **`ipa`** — IPA with tone contour digits (the default phonological
  representation used for the Wiktionary benchmark).
- **`tlc`** — thai-language.com "Enhanced Phonemic" with bracketed tone
  tags (`{M}`, `{L}`, `{H}`, `{F}`, `{R}`).
- **`morev`** — Cyrillic transliteration following the Russian-language
  tradition.

Custom schemes (Paiboon+, RTGS, or your own pedagogical convention) are
straightforward to register via `SchemeMapping`.

## Install

```bash
uv add thaiphon thaiphon-data-volubilis
# or
pip install thaiphon thaiphon-data-volubilis
```

Requires Python 3.10+. The engine itself has no runtime dependencies.
`thaiphon-data-volubilis` is a companion package shipping a ~35 k-entry
Thai lexicon derived from the VOLUBILIS Mundo Dictionary. thaiphon
detects it automatically on import and uses it for word-boundary
segmentation and word-specific readings that a rule-based engine can't
infer from orthography alone.

The data package is CC-BY-SA 4.0, separate from the Apache-2.0 engine —
installing it doesn't change the license of your own code.

### Minimal install (not recommended for most users)

If there's a hard reason to keep dependencies lean:

```bash
pip install thaiphon
```

The engine still runs, but you give up a lot:

- Accuracy on the Wiktionary IPA etalon drops from ~75 % to ~57 %.
- Common words break in visible ways. ส้ม ("orange") comes out as
  `/sa˥˩.ma˦˥/` — two syllables — when the correct reading is
  `/som˥˩/`, one closed syllable. The rules alone can't choose
  between "closed syllable with inherent /o/" and "two open syllables
  with inserted /a/"; the lexicon settles that kind of ambiguity.
- Sanskrit-learned compounds miss their learned readings.
  มหาวิทยาลัย loses the /tʰa/ insertion between /wit̚/ and /jaː/.
  The spoken form is /wit̚.tʰa.jaː.laj/; the base engine emits
  /wit̚.jaː.laj/.
- Failures are silent. The engine always returns a transliteration —
  no error, no flag, no way to tell at the output layer which of the
  ~43 % of dictionary entries fell through. A caller can't filter out
  bad results after the fact.

For anything user-facing, indexing, or searching, install both packages.

## Production footprint

`thaiphon` runs comfortably in long-lived web processes, container
orchestrators, and serverless runtimes. The engine is pure Python with
no heavyweight imports. The optional lexicon (when installed) ships as
a read-only SQLite database, memory-mapped at first use.

| Measurement                            | Engine alone | + `thaiphon-data-volubilis` |
|----------------------------------------|-------------:|----------------------------:|
| RSS after first `transcribe_sentence`  |    ~10 MiB   |                     ~19 MiB |
| RSS after 1 k lookups                  |    ~10 MiB   |                     ~47 MiB |
| Median lookup latency (lexicon hit)    |       —      |                       ~13 µs |
| Cold-start import cost                 |   negligible |                  negligible |

The ~30 MiB of additional resident set after warmup is a bounded
per-process LRU over the most recently inflated entries. The bulk of
the lexicon stays in `mmap`'d pages of `lexicon.db`, counted once by
the operating system regardless of how many Python workers have
imported the package.

### Web servers (Django, FastAPI, Flask)

Run as many workers as your CPU budget allows. The lexicon does not
multiply with worker count, because the OS page cache shares the
mapped database across processes:

- `gunicorn -w 8 myapp.wsgi` → roughly `8 × 50 MiB` of Python objects
  plus one shared `~25 MiB` for the lexicon. Not `8 × 350 MiB`.
- `uvicorn --workers 4 myapp:app` behaves the same for FastAPI and
  Starlette. Inside one worker, async handlers share a SQLite
  connection-per-thread; the threadpool gets one connection per
  active thread, opened on first use.
- Django works in sync views, async views, and `runserver` without
  any setting changes.

The lexicon loads lazily. The first request that touches a Thai word
pays the SQLite open and first index-page read (single-digit
milliseconds). Subsequent requests within that worker hit the LRU.

### Serverless (AWS Lambda, Cloud Run, Fargate)

Cold starts are dominated by your Python interpreter and your own
imports. `thaiphon` adds almost nothing at import time, since the
lexicon is only read from on first lookup. No eager inflation pass,
no `.pyc` of several hundred MiB to unpack. The 512 MiB Lambda tier
is plenty even with `thaiphon-data-volubilis` installed; the
lexicon's footprint is the on-demand LRU, not the full ~25 MiB
database.

Both packages fit comfortably in a Lambda layer.

### Multi-process scripts (`multiprocessing`, joblib, Dask)

Worker processes spawned via `fork()` (Linux/macOS default) and
`spawn()` (Windows, or after `set_start_method`) both work. The
SQLite connection is held in a `threading.local()` slot, so it never
crosses the fork boundary. Each child opens its own connection on
first use; all of them read from the same `mmap`'d file.

### Tests under `pytest-xdist`

Each xdist worker is a separate Python process. With the lexicon
installed, per-worker RSS after warmup is in the tens of MiB. Running
`-n 8` on a laptop is uneventful.

## Quick start

```python
from thaiphon import (
    transcribe, transcribe_word, transcribe_sentence, analyze, list_schemes,
)

# Discover available schemes.
list_schemes()
# ('ipa', 'morev', 'tlc')

# IPA — the phonological representation.
transcribe("ภูมิ", scheme="ipa")
# '/pʰuːm˧/'

# TLC — thai-language.com convention.
transcribe("ลิฟต์", scheme="tlc")
# 'lif{H}'

# Single-word shortcut that skips sentence tokenization.
transcribe_word("สวัสดี", scheme="ipa")
# '/sa˨˩.wat̚˨˩.diː˧/'

# Sentence segmentation + rendering.
transcribe_sentence("ฉันชอบกินข้าว", scheme="ipa")
# '/t͡ɕʰan˩˩˦/ /t͡ɕʰɔːp̚˥˩/ /kin˧/ /kʰaːw˥˩/'

# Reading profile switches colloquial vs citation pronunciation — see below.
transcribe("ลิฟต์", scheme="ipa", profile="etalon_compat")
# '/lip̚˦˥/'

# Access the intermediate phonological word.
result = analyze("ผลไม้")
for syl in result.best.syllables:
    print(syl.onset, syl.vowel.symbol, syl.vowel_length.name, syl.coda, syl.tone.name)
```

## Reading profiles

The `profile` argument controls how the engine handles foreign
phonotactics in loanwords, especially the coda consonants that native
Thai normally neutralises:

```python
# "Lift" / elevator — final /f/ from English.
transcribe("ลิฟต์", scheme="ipa", profile="everyday")         # '/lif˦˥/'
transcribe("ลิฟต์", scheme="ipa", profile="etalon_compat")    # '/lip̚˦˥/'

# "Graph" — register-sensitive.
transcribe("กราฟ", scheme="ipa", profile="everyday")          # '/kraːp̚˨˩/'
transcribe("กราฟ", scheme="ipa", profile="careful_educated")  # '/kraːf˨˩/'
```

The four supported profiles:

- **`everyday`** *(default)* — colloquial urban pronunciation. Preserves
  foreign codas in well-integrated modern loans (ลิฟต์, เคส, อีเมล) and
  collapses them in older or less register-sensitive words (กราฟ, กอล์ฟ, บัส).
- **`careful_educated`** — broadcast/formal register. Preserves more
  foreign codas than `everyday`.
- **`learned_full`** — restores full Sanskrit/Pali learned readings for
  Indic-derived words (ภูมิ, ปกติ).
- **`etalon_compat`** — dictionary-citation style; collapses every foreign
  coda to its native-Thai equivalent. Useful for matching pronunciation
  dictionaries.

Preservation is per-word, driven by the lexicon and attested usage —
not by blanket rules that paper over lexical convention.

## Accuracy

Measured by exact-match rate against independent, publicly-licensed Thai
phonology corpora:

| Corpus                       | License      | Size    | Scheme | Base engine | With `thaiphon-data-volubilis` |
|------------------------------|--------------|---------|--------|-------------|--------------------------------|
| kaikki.org Thai Wiktionary   | CC-BY-SA 4.0 | 17,014  | `ipa`  | ~57 %       | **~75 %**                      |
| PyThaiNLP G2P (Wiktionary)   | CC0          | 15,782  | `ipa`  | —           | **~73 %**                      |

"Base engine" is `pip install thaiphon` on its own. The jump to 75 %
comes from the `thaiphon-data-volubilis` lexicon (see
[Install](#install)) — the word-boundary and variant coverage a
rule-based engine can't infer from orthography alone.

Both numbers come from public data. The primary kaikki.org etalon and
PyThaiNLP's independent G2P extraction are cross-checks on each other;
if the engine breaks in a way that only one source catches, the other
will usually catch it too.

**Reproducing the numbers yourself:**

The repository ships a 2,500-entry random sample of the kaikki.org Thai
Wiktionary dump (seed 20260421, CC-BY-SA 4.0) as a bundled pytest
fixture. Install the data package, then run the etalon tests:

```bash
# Ensure both packages are installed (see Install section).
pip install thaiphon thaiphon-data-volubilis

# Bundled sample — runs in seconds, no external download:
pytest tests/etalon/test_wiktionary_ipa_sample.py -v
# Floor: 72 %.  Measured: ~74 % on the sample.

# Full 17,014-entry measurement — download the kaikki.org dump first:
#   https://kaikki.org/dictionary/rawdata.html  (Thai entries, ~43 MB)
#   Place at ~/.cache/thaiphon/kaikki-thai.jsonl  (or set $THAIPHON_KAIKKI)
pytest tests/etalon/test_wiktionary_ipa_full.py -v
# Floor: 73 %.  Measured: ~75 % on the full corpus.
```

If `thaiphon-data-volubilis` is not installed, both etalon tests skip
with a message pointing at the install command, so `make test` always
finishes cleanly whatever your setup. See `tests/README.md` for more
detail and `tests/fixtures/README.md` for fixture licensing and sampling
parameters.

## Custom schemes

```python
from thaiphon.model.enums import VowelLength
from thaiphon.renderers.mapping import SchemeMapping, MappingRenderer
from thaiphon.registry import RENDERERS
from thaiphon import transcribe

pedagogical = SchemeMapping(
    scheme_id="ped",
    onset_map={"kʰ": "kh", "k": "k", "tɕ": "j", "m": "m", "n": "n", ...},
    vowel_map={
        ("a", VowelLength.SHORT): "a",
        ("a", VowelLength.LONG):  "aa",
        ...
    },
    coda_map={"m": "m", "n": "n", "ŋ": "ng", "p̚": "p", "t̚": "t", "k̚": "k"},
    tone_format=lambda base, syl: base,   # no tone decoration
    syllable_separator="-",
)
RENDERERS.register("ped", lambda: MappingRenderer(pedagogical))

transcribe("สวัสดี", scheme="ped")
```

## Architecture

![The thaiphon pipeline — Thai text flows through normalisation, lexicon lookup, syllabification, rule-based derivation, the phonological word data contract, and the renderer, fanning out to IPA, TLC, or Morev surface strings](https://raw.githubusercontent.com/5w0rdf15h/thaiphon/v0.4.0/docs/pipeline.png)

Key design decisions:

- Zero runtime dependencies. The full phonology, rules, and lexicon ship
  as plain Python with no C or FFI.
- Immutable phonological model via frozen dataclasses.
- Lexicons (loanword overrides, Indic learned readings, royal vocabulary,
  etc.) are module-level Python literals — auditable and grep-able.
- Scheme-specific editorial conventions live in the renderer layer, not
  the phonology. Improvements to onset/vowel/coda derivation show up in
  every output scheme at once.

## Development

```bash
git clone https://github.com/5w0rdf15h/thaiphon
cd thaiphon
make dev          # install with dev dependencies via uv
make test         # pytest -q
make lint         # ruff
make typecheck    # mypy src/thaiphon
make check        # test + lint + typecheck
make format       # black + isort
```

The test suite is organised in two layers:

- **`tests/*.py`** — public API surface and representative regression
  examples. Fast, deterministic, and doubles as living API documentation.
- **`tests/rules/*.py`** — internal phonological-rule coverage (tone
  matrix, coda collapse, onset resolution, cluster strategies, lexicon
  membership, etc.). Exercises internal modules directly; the right place
  to add a regression when fixing a derivation bug.

All example words in tests are hand-curated from openly-licensed sources
only — Wiktionary (CC-BY-SA 4.0) and VOLUBILIS Mundo Dictionary
(CC-BY-SA 4.0). See `tests/fixtures/README.md` for the full provenance
policy.

## Credits

Includes code adapted from the PyThaiNLP project (Apache-2.0). See
`NOTICE` for the full attribution. Optional lexicon data in
`thaiphon-data-volubilis` derives from the VOLUBILIS Mundo Dictionary
(CC-BY-SA 4.0).

## Acknowledgements

Personal thanks to the teachers and principal at
[RTL School — Rak Thai Language School](https://rtl-school.com/) for
teaching and explaining Thai to me. This engine's phonological
intuitions come directly from their patient, careful instruction.

## License

Apache-2.0. See `LICENSE`.
