Mirandese Phonemizer

Grapheme-to-phoneme (G2P) conversion for Mirandese (mwl), the Asturleonese language of Terra de Miranda, Portugal — text in, IPA out, with cross-word sandhi, allophony and stress.

from mwl_phonemizer import phonemize

phonemize("Falo la lhéngua mirandesa.")   # 'ˈfalu lɐ ˈʎɛŋɡwa miɾɐˈndez̺ɐ.'

Install

pip install mwl_phonemizer

This pulls in orthography2ipa, which carries the Mirandese language specs and gold data.

Usage

One-shot

from mwl_phonemizer import phonemize

phonemize("lhéngua")                          # 'ˈʎɛŋɡwa'
phonemize("fuogo", dialect="mwl-x-sendim")    # Sendinese variety

phonemize caches one phonemizer per dialect, so repeated calls are cheap.

Reusable instance

from mwl_phonemizer import MirandesePhonemizer

pho = MirandesePhonemizer(dialect="mwl")
pho.phonemize("Buonos dies, cumo stás?")   # full text, punctuation preserved
pho.phonemize_word("amportante")           # a single word -> 'ɐ̃puˈɾtɐ̃tɨ'

transcribe / transcribe_word are aliases of phonemize / phonemize_word, and language_codes reports the BCP-47 codes the instance covers — the surface downstream engines call.

Dialects

The dialect argument is an orthography2ipa Mirandese spec code:

MirandesePhonemizer("mwl-x-sendim").phonemize("lhobo")   # 'ˈloβu', not 'ˈʎobu'

How it works

The transcription is the orthography2ipa Mirandese pronunciation lattice. That engine owns the phonology — grapheme rules, allophony, cross-word sandhi and stress — for all three lects. This library is a thin Mirandese-facing wrapper that adds dialect selection, punctuation-preserving text handling, and two opt-in layers:

• Lexicon overlay (lookup=True) — a bundled native-speaker word dictionary (mwl_phonemizer.gold, from the TigreGotico/mirandese_g2p dataset). Words present in it are returned verbatim. Its transcription convention is finer-grained (marking, for example, vowel centralisation) and differs from the sentence gold below, so it is off by default.

  pho.phonemize("lhéngua")               # 'ˈʎɛŋɡwa'   (lattice)
  pho.phonemize("lhéngua", lookup=True)  # 'ˈʎɛ̃ɡwɐ'   (dictionary)
  

• CRF correction (use_crf=True) — a linear-chain CRF over the engine's per-grapheme feature export, trained on that same word dictionary. It is tuned to the dictionary's convention and moves output away from the sentence gold, so it too is off by default; it is kept for callers whose target matches that convention.

  MirandesePhonemizer("mwl", use_crf=True).phonemize_word("amportante")
  

Accuracy

Phoneme Error Rate (PER = character edit distance / gold length), gold lookup disabled so the numbers reflect the model.

Human gold — the only accuracy measurement (primary)

The only human-authored Mirandese gold is the 219-word native-speaker dictionary TigreGotico/mirandese_g2p (central 206, sendinese 11, raiano 2; rows routed to mwl / mwl-x-sendim / mwl-x-ifanes by their dialect tag). Everything below is scored against it, full dataset, no caps. Three normalisations are reported:

• strict — only structural markers (syllable dots, optional-phoneme parentheses) removed; stress and every diacritic count.
• folded — additionally folds three documented notation conventions: stress marks (ˈ ˌ), length (ː), and tie-bars (t͡ʃ→tʃ).
• broad — additionally folds the documented broad-vs-narrow gap: the human gold is a narrow transcription, this engine is broad-phonemic. Folds centralised ʉ ʊ→u, spirant ð→d, dark ɫ→l, lowered e̞→e, and the apical/laminal sibilant diacritics (s̺ s̻→s). What remains is the residual true phonemic error, not convention distance.

Reading the table honestly:

• Lexicon lookup ≈ 0% is pure memorisation — the lexicon is this gold, so every word is returned verbatim. It is not an accuracy signal, and it mixes the narrow lexicon convention into otherwise-broad sentences, which is why it is off by default.
• CRF fit-to-dictionary (3.83% folded) is trained and scored on the same words — a circular upper bound, not accuracy.
• CRF 5-fold CV (18.16% folded) is the honest out-of-dictionary estimate. It edges the lattice by ~1.3pp folded, but on the convention-neutral broad basis the gap collapses to 0.37pp (12.73% vs 13.10%): almost all of the CRF's apparent gain is matching the lexicon's narrow convention, not fixing real errors — and it couples every output to that convention. Hence the pure lattice remains the default: convention-neutral, deterministic, untrained, and statistically tied with the CRF on true phonemic error.

Reproduce with:

python -m mwl_phonemizer.evaluate                # dialect mwl
python -m mwl_phonemizer.evaluate mwl-x-sendim

Engine sentence set — a consistency check, NOT an accuracy claim

orthography2ipa also ships 20-sentence sets per lect (mwl/mwl-x-sendim/mwl-x-ifanes). The lattice reproduces them at ~0% PER — but those sentences were authored to match this engine's own output (they are engine-pinned), so that 0% is an internal consistency check, not a measure of accuracy against human ground truth. Earlier versions of this README (and two downstream dataset cards) presented that 0% as the primary accuracy figure; that was circular. The human-gold table above is the real measurement.

License

Apache-2.0