caesura-breaks

Predicts prosodic phrase breaks on lowercase, punctuation-free English text, one label per word boundary: none, b2 (minor break) or b3 (major break). It is the learned half of caesura, a TTS front-end stage that has to decide where the voice pauses when the input arrives without reliable punctuation, which is the normal case for phone transcripts and LLM output.

Labels

id	label	meaning
0	none	no break after this word
1	b2	minor break, roughly a comma-level juncture
2	b3	major break, roughly a sentence-level juncture

b1 exists in the break inventory of the wider project but is not predicted here, because it cannot be derived from punctuation.

The training labels are punctuation, not prosody

This is the most important thing to know before using the model. There is no free ToBI-annotated corpus of usable size, so the labels come from the punctuation in LibriTTS-R's original transcripts: comma, semicolon, colon, dash and bracket become b2; full stop, question mark and exclamation mark become b3; everything else is none. Punctuation and prosody agree often but not always. Breaks that a reader takes at no punctuation site at all, which is where the interesting front-end failures live, are systematically labelled none in training. The model therefore inherits a bias against exactly the breaks that matter most. The repository quantifies this on a hand-annotated adversarial set.

Training data

LibriTTS-R train-clean-100 transcripts, consecutive same-chapter utterances stitched into passages of about 45 tokens so that b3 occurs mid-sequence rather than only on the final token. 10196 passages, roughly 576k boundaries, about 8.7% b2 and 6.4% b3.

Results

Per-boundary F1, from the repository's evaluation:

test set	b2 F1	b3 F1	any-break F1
libritts_test	66.0	77.5	83.1
switchboard	34.2	95.3	81.4
adversarial	28.9	96.8	75.0

switchboard is out-of-domain conversational text; adversarial is 60 hand-annotated sentences built to break syntactic break prediction, so the drop there is the point of the set rather than a defect of the model.

Usage

from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

tok = AutoTokenizer.from_pretrained("daltonoscar0/caesura-breaks", add_prefix_space=True)
model = AutoModelForTokenClassification.from_pretrained("daltonoscar0/caesura-breaks")

words = "the man in the grey coat is my uncle".split()
enc = tok(words, is_split_into_words=True, return_tensors="pt")
with torch.no_grad():
    logits = model(**enc).logits[0]

# The label for a word sits on that word's LAST subword, not its first.
last = {}
for pos, wid in enumerate(enc.word_ids()):
    if wid is not None:
        last[wid] = pos
for i, word in enumerate(words):
    print(word, model.config.id2label[int(logits[last[i]].argmax())])

Or through the project, which adds the rule system and emphasis marking:

pip install git+https://github.com/daltonoscar0/caesura
python -m caesura --system both "the man in the grey coat is my uncle"

Training details

base model	distilroberta-base
epochs	1
batch size	16
learning rate	5e-05
schedule	one-cycle, 10% warmup
seed	17
selection	dev macro-F1 over b2 and b3 only
wall clock	5594.1 s on cpu

Labels are attached to the last subword of each word and every other subword position is masked to -100.

Limitations

• Trained on read speech from public-domain literature. Conversational input is out of domain and scores lower.
• The label set has no b1, so no minor juncture is available to the caller.
• Emphasis is not predicted by this model at all; the repository marks emphasis with rules over a dependency parse.
• On garden-path sentences the model places breaks consistent with the locally favoured reading rather than the globally correct one. The repository documents this case by case.

Licence

MIT.