π LEHJA β build status
Updated 2026-06-13 12:54:01 UTC Β· auto-refresh 60s Β· cron every 5 min
OVERALL (Arabic track + MT): 28%
H100 NVL: 100% GPU Β· active jobs 9 Β· VRAM 33.7 GB used / 59.4 GB free of 93.6 GB
β MINING LIVE β harvesting clean recitation clips with perfect tashkeel labels.
π Tasks β main & sub
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β¦. Urdu voice model (previous milestone) 100% β
CosyVoice2 Urdu LoRA trained + delivered Β· validation PASS Β· median CER 0.065 Β· model on app-server
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1. Build Quranic-Arabic pipeline (canonical alignment, full tashkeel) 100% β
Diacritic-preserving aligner + mining/prep/train/validate scripts
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2. Canonical Uthmani Quran text + alignment index 100% β
6236 ayahs, full Uthmani Hafs (zabar/zer/pesh/shadda/tanwin/ghunna)
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Skeleton search index β rough transcript β exact ayah span
π 3. Mine in-domain Arabic recitation (your QTM corpus) 53% π whisper-ar VAD-segment + inline Quran align Β· 6,400/12,000 files Β· 0/8 shards done
π clean recitation clips harvested Β· 6,409 clips Β· 695.1 min Β· 2,126 distinct ayahs
π 4. Canonical-align β diacritized labels + confidence gate 53% π perfect diacritized labels (alignment inline per clip)
β¬ high-confidence gate (matchβ₯85) + dedup Β· 4,162 train-grade clips
β¬ 5. Train + validate + package Quranic Arabic LoRA 0% β¬ CosyVoice2 fine-tune (learn diacritized recitation) Β· 25 epochs planned
β¬ validate: recites correct Arabic? (whisper-readback)
β¬ package CosyVoice2-0.5B-ar + download to app-server
β¬ MT. Flagship MT benchmark (Qwen3.5 vs qwen3:14b) 0% β¬ latency + Urdu/Arabic quality on real tutor sentences
π Arabic data harvest
Recitation clips mined: 6,409 Β· 695.1 min clean audio
Train-grade (matchβ₯85): 4,162 clips Β· 2,126 distinct ayahs covered
Source: your QTM Quran-recitation corpus (30,165 Arabic files available)
Approach: every clip's rough transcript is matched to the canonical Uthmani Quran text, so labels carry perfect diacritics (zabar/zer/pesh/tashdeed/tanwin/ghunna) β and non-recitation (Urdu explanation, repetition) is auto-filtered.