A full fine-tuned Whisper Large v3 Turbo model specialized for Quranic Arabic recitation (tilawah) recognition. Outputs fully diacritized Arabic text (tashkeel/harakat).

Evaluation Results

Metric	Score
WER (normalized)	0.05%
WER (diacritized)	0.05%
Eval samples	500
Best checkpoint	Step 8,000

Training Progression

Step	Train Loss	Val Loss	WER (diacritized)	WER (normalized)
200	0.1394	0.0796	15.10%	12.97%
1,000	0.0396	0.0200	4.76%	4.26%
2,000	0.0289	0.0103	2.38%	2.13%
3,000	0.0171	0.0047	1.11%	1.01%
5,000	0.0129	0.0025	0.71%	0.66%
7,000	0.0111	0.0009	0.20%	0.20%
8,000	0.0108	0.0004	0.05%	0.05%
9,000	0.0103	0.0005	0.15%	0.15%

Comparison with Previous Models

Model	Method	WER (norm)	WER (diac)	Params
Tarteel AI baseline	-	5.75%	-	-
whisper-large-v3-quran-lora	LoRA (r=32)	1.58%	3.92%	1.5B
whisper-turbo-quran	Full fine-tune	0.05%	0.05%	809M

Model Details

Property	Value
Base model	openai/whisper-large-v3-turbo (809M params)
Method	Full fine-tune (all parameters)
Encoder initialization	Transferred from whisper-large-v3-quran-lora
Dataset	tarteel-ai/everyayah (~127K samples)
Augmentation	Speed perturbation + Gaussian noise + Reverb simulation + SpecAugment
Precision	bf16 (mixed precision)
Optimizer	AdamW fused (cosine LR schedule)
Learning rate	1e-5
Effective batch size	64 (16 x 4 gradient accumulation)
Training steps	~9,900 (~5 epochs)
GPU	NVIDIA A100 40GB
Output	Fully diacritized Arabic (tashkeel)

Encoder Transfer

This model uses a unique warm-start strategy. The encoder weights were initialized from a previously trained whisper-large-v3-quran-lora model. Since Whisper Large v3 and Whisper Large v3 Turbo share the same encoder architecture, the Quran-specialized encoder weights were directly transferred, giving the model a significant head start in recognizing Quranic recitation patterns, tajweed rules, and maqam variations.

Usage

python

from transformers import WhisperProcessor, WhisperForConditionalGeneration

processor = WhisperProcessor.from_pretrained("baristiran/whisper-turbo-quran")
model = WhisperForConditionalGeneration.from_pretrained("baristiran/whisper-turbo-quran")

inputs = processor(audio_array, sampling_rate=16000, return_tensors="pt")
predicted_ids = model.generate(**inputs, max_length=448)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
# Output: fully diacritized Arabic text

Tilavet

This model is the core engine of Tilavet — a next-generation Quran companion app that listens, understands, and guides.

Tilavet recognizes Quranic recitation in real time, automatically identifies the exact ayah being recited, and follows along as the reader continues. It translates into any language, detects recitation errors, and provides instant feedback to help users improve their reading.

Built for everyone who engages with the Quran — huffaz refining their memorization, students learning proper tajweed, individuals following along during taraweeh or daily reading sessions, and anyone who wants a deeper, more interactive connection with the text.

Key capabilities:

Real-time ayah recognition and tracking (prompter mode)
On-device processing — fully offline, no internet required
Diacritized Arabic output with multi-language translation
Recitation error detection and tajweed guidance
Automatic re-synchronization when the reader skips or repeats ayahs

Technical highlights:

CoreML deployment via WhisperKit (iOS, Apple Neural Engine)
~400 MB on-device model (INT8 quantized)
~3-4 second initial recognition, real-time tracking thereafter
iPhone 12+ and newer supported

Developer

Tarık İsmet ALKAN — GitHub

License

Apache 2.0