---
license: cc-by-nc-4.0
language:
- ar
tags:
- automatic-speech-recognition
- wav2vec2
- ctc
- quran
- tajweed
- phoneme-recognition
- ipa
base_model: quranic-orgz/omni-ctc-ipa-v7
pipeline_tag: automatic-speech-recognition
---

# omni-ctc-ipa-v7-attraux-aux0p2-20260928

Wav2Vec2-CTC Quranic phoneme recognizer. Fine-tunes **`omni-ctc-ipa-v7`** with an
**attribute auxiliary loss (AttrAux)** at weight **0.2**, part of the `attraux-v7`
research roadmap (Stage 1 v7-recipe foundation, seed 42, trained 2026-09-28).

## Model description

- **Architecture**: `Wav2Vec2ForCTC`, 24 transformer layers, hidden size 1024, 71-token
  IPA-style phoneme vocabulary (Tajweed-aware: ghunna/tafkheem/gemination/qalqalah symbols
  included).
- **Base checkpoint**: `omni-ctc-ipa-v7` (frozen feature encoder, transformer layers 0-11
  frozen during this fine-tune, layers 12-23 trainable).
- **Training data**: `hetchyy/everyayah-phonemes` (207,114 max train rows), 2,400 steps,
  batch size 1 x grad-accum 16, LR 1e-5, 240 warmup steps, fp16.
- **AttrAux mechanism**: an auxiliary BCE loss over four pooled attribute channels
  (ghunna/tafkheem/gemination/qalqalah), derived by pooling the existing CTC logits
  (not a separate model head), weighted at **0.2** relative to the primary CTC loss,
  with attribute-group class rebalancing enabled (`--attr-aux-group-rebalance`).
- **Best training-time eval PER**: 1.807% (`hetchyy/everyayah-phonemes` dev split).

## Insights

**Lowest pooled PER of all 17 `attraux-v7`/baseline checkpoints evaluated** on a
professional-reciter benchmark suite (quranmd + tadabur + mufti_malhan + quranlab,
187,417 pooled phoneme units):

| Metric (pooled, 4 professional sets) | Value |
|---|---|
| PER | **6.02%** (lowest of all 17 evaluated models) |
| Substitution / Deletion / Insertion | 2.39% / 1.17% / 2.46% |
| Ghunna F1 | 97.05% |
| Qalqalah F1 | 95.33% |
| Tafkheem F1 | 97.85% |
| Gemination F1 | 98.03% |
| Sifat-core F1 | 97.99% |
| Macro F1 (all families) | 97.67% |

For context, its Macro F1 (97.67%) sits just under the two higher-F1 siblings from
the same sweep — `omni-ctc-ipa-v7-attraux-aux0p1-20260928` (PER 6.09%, F1 97.72%) and
`omni-ctc-ipa-v7-attraux-aux0p4-20260928` (PER 6.07%, F1 97.74%, also the highest
Macro-F1 of all 17). This checkpoint is the pick if minimizing raw transcription
error rate is the priority; the aux0p4 sibling is the pick if Tajweed-family F1
matters more. The gap between all three is small (<0.1pp F1, <0.1pp PER) and likely
within run-to-run noise for this recipe.

**Caveat — read before use as evidence of AttrAux working:** the parent research
roadmap (`docs/roadmaps/attraux-v7/ROADMAP.md`) evaluated this exact aux-weight
(w=0.2, Stage 1) against a matched no-aux control on the roadmap's primary metric —
learner-correction precision (LCP) on a speaker-split QuranMB.v2 dev set with a paired
bootstrap CI — and found **no AttrAux arm across the full sweep (w=0.05-0.4) produced
a statistically significant LCP gain over control** (overall roadmap KILL on
2026-09-29 after Stages 1-5' exhausted every tested AttrAux configuration, including
hard-negative frame weighting, density oversampling, and FP-mined replay). The
pooled-PER/tajweed-F1 numbers above measure professional-recitation transcription
quality — where this is the single best-PER checkpoint tried — but should **not** be
read as evidence that AttrAux training improved mispronunciation-detection precision;
that hypothesis was tested directly and rejected.

In short: the best general-purpose Quranic phoneme transcriber by raw PER found in
this sweep, ahead of its own base model (`omni-ctc-ipa-v7`: PER 6.37%, F1 97.50%) and
every other `attraux-v7`/legacy checkpoint benchmarked, but its AttrAux loss did not
measurably improve mispronunciation-detection quality over a plain no-aux fine-tune at
the same recipe.

## Intended use

Quranic recitation phoneme transcription (near-offline/offline ASR) and Tajweed rule
classification (ghunna/tafkheem/gemination/qalqalah) from audio. Not validated as a
mispronunciation-detection/correction system — use `omni-ctc-ipa-v7` or a control
checkpoint from the same sweep for that use case pending further evidence.

## Training hyperparameters

```
dataset: hetchyy/everyayah-phonemes
base_model: omni-ctc-ipa-v7
max_steps: 2400
batch_size: 1, grad_accum: 16
learning_rate: 1e-5, warmup_steps: 240
freeze_transformer_layers: 12 (of 24)
attr_aux_weight: 0.2
attr_aux_warmup_steps: 240
attr_aux_group_rebalance: true
seed: 42
fp16: true
```