Tilavet Aligner

Small MVP package for monotonic Quran phoneme alignment.

It consumes tilavet-phonemizer alignment targets:

target = Phonemizer().phonemize("رَيْبَ ۛ فِيهِ").to_alignment_dict()

and aligns frame-level phoneme log probabilities to that known target sequence. This package does not perform free ASR decoding.

MVP Modules

• contract.py: validates the phonemizer alignment target JSON.
• posterior.py: adapts sparse JSON, dense JSON, dense logits, and optional .npy matrices to frame log-prob dictionaries.
• viterbi.py: aligns frame log-probabilities to target phoneme symbols, including optional blank-aware CTC mode.
• word_spans.py: converts symbol frame spans to word-level timestamps.
• ctc.py: vocabulary and frame normalization helpers.

Posterior Inputs

Sparse frame dictionaries:

[
  {"a": -0.1, "b": -5.0},
  {"a": -4.0, "b": -0.1}
]

Dense matrix JSON or .npy input requires a vocabulary file whose order matches the posterior columns:

["<blank>", "a", "b"]

CLI examples:

tilavet-align target.json sparse-frames.json
tilavet-align target.json dense-logprobs.json --vocab-json vocab.json
tilavet-align target.json dense-logits.npy --vocab-json vocab.json --from-logits
tilavet-align target.json ctc-logprobs.json --ctc --blank-symbol "<blank>"

--ctc uses expanded states [blank, s0, blank, s1, ...]; blank frames do not expand word-level symbol spans.

Output Confidence

Alignment output keeps the raw Viterbi score and adds normalized confidence fields:

{
  "score": -0.6,
  "frame_count": 5,
  "mean_log_prob": -0.12,
  "confidence": 0.8869,
  "words": [
    {
      "token": "ab",
      "score": -0.2,
      "scored_frames": 2,
      "mean_log_prob": -0.1,
      "confidence": 0.9048
    }
  ]
}

In CTC mode, frame_count may include blank frames inside the timestamp span; scored_frames counts only frames assigned to the word's target symbols.

Synthetic End-to-End Demo

With the sibling phonemizer source tree present:

PYTHONPATH=src python3 examples/synthetic_alignment.py \
  --phonemizer-src "../Tilavet Phonemizer/src"

This runs:

Arabic text -> phonemizer.to_alignment_dict() -> synthetic CTC posterior -> word timestamps

It is not an acoustic-model test; it is a package-boundary smoke test.

Development

python3 -m pytest -q
python3 -m ruff check .