Mirandese Phonemizer

Grapheme-to-phoneme (G2P) conversion for Mirandese (mwl), the Asturleonese language of Terra de Miranda, Portugal.

The pipeline has three layers, each falling back to the next:

1. Native-speaker gold dictionary — pronunciations from the TigreGotico/mirandese_g2p dataset, bundled as a word list and returned verbatim.
2. orthography2ipa lattice — the language-agnostic orthography2ipa engine with its Mirandese language specs provides the base transcription for any word.
3. CRF correction — a linear-chain CRF trained on the gold dictionary corrects the lattice output for out-of-dictionary words. Its features are orthography2ipa's per-grapheme feature export (phonological-class predicates, grapheme context, candidate-lattice top-1/cost, per-word confidence). Stress placement is delegated to the spec's own stress rules, so the CRF only learns segment corrections.

Quickstart

from mwl_phonemizer import MirandesePhonemizer

pho = MirandesePhonemizer(dialect="mwl")
pho.phonemize("lhéngua")                      # 'ˈʎɛ̃ɡwɐ'
pho.phonemize("Falo la lhéngua mirandesa.")   # full text, punctuation kept
pho.phonemize_word("amportante")              # single word

Or the module-level convenience (caches one phonemizer per dialect):

from mwl_phonemizer import phonemize

phonemize("lhéngua")
phonemize("fuogo", dialect="mwl-x-sendim")

MirandesePhonemizer also implements the orthography2ipa G2PPlugin interface (transcribe, transcribe_word, language_codes).

Dialects

The dialect argument takes an orthography2ipa Mirandese spec code:

Sendinese gold overrides (e.g. lh → /l/ words) are layered on top of the base gold dictionary. A small Raiano word list is bundled in mwl_phonemizer.gold.RAIANO; it is not wired to a dialect because no mwl-x-raiano spec exists in orthography2ipa.

Accuracy

Phoneme Error Rate (PER = character edit distance / gold length) on the full 205-word native-speaker gold dictionary, dialect mwl, gold lookup disabled so the numbers reflect the models rather than the dictionary:

Methodology, stated honestly:

• fit to gold — the CRF is trained on the full gold dictionary and scored on that same dictionary. This is an upper bound (the deployed default trains exactly this way), not a generalization estimate.
• 5-fold cross-validated — every gold word is scored by a CRF trained without it. This estimates performance on out-of-dictionary words: the CRF helps on both metrics even for words it has never seen, and words that are in the dictionary bypass the model entirely via gold lookup.
• Most residual stressed-PER error is stress-mark placement, which comes from the spec's rule-based stress detector, not from the CRF.

Reproduce with:

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

Retraining the CRF

The CRF trains at construction time in a few seconds; there is nothing to ship. To persist and reuse a model:

pho = MirandesePhonemizer(dialect="mwl", crf_model_path="mwl.crf")

The model is loaded from the path when the file exists and trained-then-saved otherwise. To train on custom data:

from orthography2ipa import G2P
from mwl_phonemizer.crf import CRFCorrector

crf = CRFCorrector(G2P("mwl")).train([("lhéngua", "ˈʎɛ̃gwɐ")])
crf.predict("lhéngua")
crf.save("custom.crf")

Install

pip install mwl_phonemizer

License

Apache-2.0