---
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-aux0p1-20260928

Wav2Vec2-CTC Quranic phoneme recognizer. Fine-tunes **`omni-ctc-ipa-v7`** with an
**attribute auxiliary loss (AttrAux)** at weight **0.1**, part of the `attraux-v7`
research roadmap (Stage 2 aux-weight dose sweep, 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 **0.1** relative to the primary CTC loss,
  with attribute-group class rebalancing enabled (`--attr-aux-group-rebalance`).
- **Best training-time eval PER**: 1.970% (`hetchyy/everyayah-phonemes` dev split).

## Insights

This checkpoint is one of two survivors (out of 17 evaluated `attraux-v7` variants
plus baselines) that beat the joint bar **pooled PER < 6.10% AND pooled tajweed
macro-F1 > 97.70%** on a professional-reciter benchmark suite (quranmd + tadabur +
mufti_malhan + quranlab, 187,417 pooled phoneme units):

| Metric (pooled, 4 professional sets) | Value |
|---|---|
| PER | 6.09% |
| Substitution / Deletion / Insertion | 2.45% / 1.05% / 2.59% |
| Ghunna F1 | 97.16% |
| Qalqalah F1 | 95.64% (best qalqalah score across all 17 evaluated models) |
| Tafkheem F1 | 97.85% |
| Gemination F1 | 98.01% |
| Sifat-core F1 | 98.01% |
| Macro F1 (all families) | 97.72% |

**Caveat — read before use as evidence of AttrAux working:** the parent research
roadmap (`docs/roadmaps/attraux-v7/ROADMAP.md`) evaluated this exact aux-weight sweep
(w=0.05/0.1/0.4) 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 arm, including this one, produced a statistically
significant LCP gain over control** (Stage 2 verdict: REGRESSION/NO EFFECT for all
three weights; overall roadmap KILL on 2026-09-29 after Stages 1-5' exhausted every
tested AttrAux configuration). The pooled-PER/tajweed-F1 numbers above measure
professional-recitation transcription quality, which this checkpoint is good at, but
should **not** be read as evidence that its AttrAux training signal improved
mispronunciation-detection precision — that hypothesis was tested and rejected on a
different, dedicated benchmark.

In short: a solid general-purpose Quranic phoneme transcriber, marginally ahead of its
own base model (`omni-ctc-ipa-v7`: PER 6.37%, F1 97.50%) and of every other
`attraux-v7` variant tried, 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: 0.1
attr_aux_warmup_steps: 240
attr_aux_group_rebalance: true
seed: 42
fp16: true
```