Superseded: kristijonas/banano-kirtis-v2 (xlm-roberta-large, trained on normative written-text stressing) scores 99.4 % word-exact on a 3,000-sentence held-out set where this model scores 85.4 % through the same pipeline. Use v2 unless you specifically want speech-style unstressed clitics.

Banano kirtis 🍌 — Lithuanian stress annotator (kirčiuoklis)

Predicts, for every word in a sentence, which syllable carries the stress and which accent type it has (trumpinis/grave, tvirtapradė/acute, tvirtagalė/tilde) — using sentence context, not a lookup table.

Continuing the vegetable-garden lineage of open Lithuanian speech models (svogūnas 🧅 → paprika 🌶️ → bananas 🍌).

What it is

XLM-RoBERTa-base token classification, 37 classes:

0                      unstressed (clitics / function words)
1 + syllable*3 + acc   acc: 0 = short (grave), 1 = acute, 2 = tilde
                       syllable index 0..11, first sub-token of each word

Trained on ~1.08 M sentences from LIEPA-3 phon (stress recovered from the case-encoded SAMPA transcriptions) plus 162 k lexicon forms derived from svogunas/g2p-lt-lexicon.

Evaluation — read this before trusting any number

Internal held-out set (grouped by recording, same labelling convention as training): 99.4 % exact, 99.4 % position, 99.5 % accent type.

Treat that as a sanity check, not as accuracy. It measures agreement with our own labelling convention, so it cannot detect a systematic error in that convention — and it did not: an earlier version scored equally well while silently predicting "no stress" for words it had never seen. The fix was a labelling correction (274 580 labels where the corpus simply omits a stress mark were reclassified as unknown and excluded from the loss, instead of being trained as "unstressed").

No gold benchmark for Lithuanian stress exists, open or otherwise. Nothing here is validated against expert-adjudicated data. Spot-checking against other tools on a handful of sentences is what we have, and it is too small a sample to publish as a comparison — so we do not. If you have, or would help build, an expert-annotated test set, please get in touch; that is the single most useful thing anyone could contribute to Lithuanian accentuation right now.

Known limitations

• Foreign-origin surnames are unreliable (e.g. -sonas); the model signals this with low confidence — use the confidence value.
• Function words (ir, per, su, iš) are predicted unstressed. This follows spoken Lithuanian, where they are clitics. Normative tools mark them. Neither is wrong; pick per use case (TTS → this model; teaching correct citation forms → a rule-based tool).
• ~9 % of words differ from VDU in accent type while agreeing on position.
• No gold benchmark for Lithuanian stress exists. Nothing here is validated against expert-adjudicated data, because no open set exists to validate against. Numbers are agreement, not accuracy.

Usage

import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer

MD = "kristijonas/banano-kirtis"
tok = AutoTokenizer.from_pretrained(MD)
model = AutoModelForTokenClassification.from_pretrained(MD).eval()

words = "vyriausybė svarsto naują įstatymą".split()
enc = tok([words], is_split_into_words=True, return_tensors="pt")
with torch.no_grad():
    pred = model(**enc).logits.argmax(-1)[0]

first = {}
for pos, wid in enumerate(enc.word_ids(0)):
    if wid is not None and wid not in first:
        first[wid] = pos
for i, w in enumerate(words):
    p = int(pred[first[i]])
    if p == 0:
        print(w, "→ unstressed")
    else:
        syl, acc = (p - 1) // 3, ["short", "acute", "tilde"][(p - 1) % 3]
        print(f"{w} → syllable {syl + 1}, {acc}")

Rendering the Unicode combining mark onto the right vowel is a separate presentation step, and it is where the real difficulty is — see Placing the mark below for the four rules and a working implementation.

Placing the mark (read this before using the output)

The model predicts which syllable and which accent. Turning that into correctly marked text is a second problem, and it is the one that will cost you an afternoon — these rules were each learned by shipping the wrong output first.

What the model returns

XLMRobertaForTokenClassification, 37 labels, one prediction per word (take the FIRST subword token of each word):

p = int(logits.argmax(-1)[first_subword_index])
if p == 0:
    stress = None                     # word carries no mark
else:
    syllable = (p - 1) // 3           # 0-based index into the word's nuclei
    accent   = ["short", "acute", "tilde"][(p - 1) % 3]

short = trumpinė (◌̀), acute = tvirtapradė (◌́), tilde = tvirtagalė (◌̃).

Where the mark goes

syllable indexes the word's syllable nuclei, so you must split the word the same way the labels were made. Four rules, each of which we got wrong at first:

1. Only true diphthongs merge into one nucleus: ai au ei ui ie uo (plus oi ou eu in loans). Every other vowel pair is hiatus — two syllables. teatras is te‑a‑tras (3), not tea‑tras (2). Getting this wrong silently shifts the accent on every Latinate loan: we shipped teatrãs for teãtras.
2. A sonorant (l m n r) joins the nucleus only after a SHORT vowel (a e i u) and only when no vowel follows: kal̃nas, ir̃. After a long vowel it is a plain coda, so the mark stays on the vowel: dė̃l, not dėl̃.
3. i before another vowel is a palatalization glide, not a nucleus member — in ia io iu iau iai iuo the accent belongs to the vowel(s) after it: dirbtiniù, vėliaũ, žaliaĩ. Note ie is a REAL diphthong and is excluded (priẽš, píeva).
4. Within a two-element nucleus: tilde marks the SECOND element (kaĩp, priẽš), acute the FIRST (káulas, píeva). For a mixed diphthong the tilde sits on the sonorant (ir̃, kar̃tu).

A working implementation

Copy freely.

VOW = set("aeiouyąęėįųū"); SON = set("jlmnr"); SHORT_VOW = set("aeiu")
DIPH = {"ai","au","ei","ui","ie","uo","oi","ou","eu"}
COMB = {"short": "\u0300", "acute": "\u0301", "tilde": "\u0303"}

def nucleus_groups(word):
    w, groups, cur = word.lower(), [], []
    for i, c in enumerate(w):
        nxt = w[i+1] if i+1 < len(w) else ""
        if c in VOW:
            if not cur:
                cur = [i]; continue
            prev = w[cur[-1]]
            glide = (len(cur) == 1 and prev == "i" and cur[0] > 0
                     and w[cur[0]-1] not in VOW)
            if prev + c in DIPH or glide:
                cur.append(i)
            else:
                groups.append(cur); cur = [i]
        elif cur and c in SON and nxt not in VOW and w[cur[-1]] in SHORT_VOW:
            cur.append(i); groups.append(cur); cur = []
        else:
            if cur: groups.append(cur)
            cur = []
    if cur: groups.append(cur)
    return groups

def mark_word(word, syl, acc):
    groups = nucleus_groups(word)
    if not groups or syl >= len(groups):
        return None                      # cannot place: leave the word bare
    g = groups[syl]
    vs = [i for i in g if word[i].lower() in VOW]
    if len(vs) >= 3 and word[vs[0]].lower() == "i":
        vs = vs[1:]                      # iau / iai / iuo
    elif (len(vs) == 2 and word[vs[0]].lower() == "i"
          and (word[vs[0]] + word[vs[1]]).lower() in ("ia", "io", "iu")):
        vs = vs[1:]                      # ia / io / iu, but NOT ie
    if acc == "tilde" and len(g) > 1:
        pos = vs[1] if len(vs) >= 2 else g[-1]
    else:
        pos = vs[0] if vs else g[0]
    return word[:pos+1] + COMB[acc] + word[pos+1:]

Things you should probably also do

• Do not accent what is not Lithuanian. Acronyms (JAV), digits, single letters (D.) and foreign names should be passed through untouched. We emitted Ãxios before adding a skip rule.
• Abstain outside the training vocabulary. The model returns a confident label for any input, including words it has never seen — it rendered kirčiuoklis as kirčiuõklis at 0.999 confidence. Confidence is not evidence of knowledge. Leaving unknown words unmarked is more useful than a fluent guess.
• Put a lexicon in front of it. A dictionary lookup beats the model on any word the dictionary has; use the model for what a dictionary cannot do — unseen words and homograph disambiguation from context.

Attribution

• Base: FacebookAI/xlm-roberta-base (MIT)
• Speech data: LIEPA-3 (CC BY 4.0, VDU / CLARIN-LT) — ačiū už atvirus duomenis
• Lexicon: svogunas/g2p-lt-lexicon (CC BY 4.0)
• Built by the Kalamo team.

Feedback, and especially hard Lithuanian test cases, are very welcome.