Unlike standard Arabic ASR systems that output orthographic Arabic text, this model outputs a phonetic (sound-level) representation, making it suitable for:

Tajweed research and education
Word-level pronunciation analysis
Forced alignment of Quranic recitation
Linguistic studies of Classical Arabic phonetics

To the best of our knowledge, this is the first publicly released wav2vec2 model trained explicitly for Quranic phonetic transcription.

Key Features

🎙️ Outputs phonetic strings, not Arabic text
📖 Trained on Quranic recitation, not conversational Arabic
🔤 Custom phonetic vocabulary + tokenizer
🧠 Compatible with CTC forced alignment
🧩 Optimized for word-level Tajweed analysis

Intended Use

This model is intended for:

Quranic pronunciation analysis
Tajweed educational tools
Phoneme-level alignment
Academic research on Quranic recitation

⚠️ Not intended for:

Modern Arabic ASR
End-to-end Tajweed grading of full ayat
Automatic religious judgments
Replacement of qualified Quran teachers

Model Architecture

Base model: facebook/wav2vec2-base
Training objective: CTC (Connectionist Temporal Classification)
Sampling rate: 16 kHz
Input: Mono audio waveform
Output: Phonetic transcription string
Tokenizer: Custom character-level phonetic tokenizer

Training Dataset

📦 Dataset Used

Buraaq/quran-md-words

This dataset contains word-level Quranic recitation audio with aligned metadata.

Each sample includes:

Audio of a single Quranic word
Phonetic transcription (word_tr)
Arabic word (word_ar)
Surah and ayah identifiers
Word index within the ayah

This dataset was chosen intentionally to:

Preserve clear phonetic boundaries
Avoid coarticulation noise from full-ayah audio
Enable high-accuracy phonetic learning

🔗 Dataset: https://huggingface.co/datasets/Buraaq/quran-md-words

Phonetic Vocabulary & Tokenizer

The model uses a custom phonetic vocabulary built directly from the dataset:

Vocabulary constructed from all unique characters in word_tr
Character-level CTC tokenizer
Includes:
- | as word delimiter
- [PAD] for CTC blank
- [UNK] for unknown symbols

This design allows:

Robust forced alignment
Fine-grained phonetic decoding
Word boundary detection via delimiter token

Training Procedure

Training Setup

Framework: Hugging Face Transformers + Datasets
Platform: Windows-safe (no multiprocessing crashes)
Audio handling: Hugging Face Audio feature
Batch size: 16
Epochs: 20
Learning rate: 2e-5
Optimizer: AdamW (default Trainer)
Precision: FP16 when CUDA available
Train / Eval split: 90% / 10%

The wav2vec2 feature extractor was not frozen, allowing adaptation to Quranic recitation acoustics.

Training Results

On the held-out evaluation split:

Training accuracy: 99.7%
Evaluation accuracy: 99.8%
Test split size: 10% of the dataset

⚠️ Note:
These results reflect word-level phonetic transcription accuracy on a dataset with consistent recitation style.
Performance may degrade on:

Fast recitation

Strong coarticulation

Unseen riwayat styles

Example Usage

Load Model and Processor

python

from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import torch
import soundfile as sf
import librosa
import numpy as np

processor = Wav2Vec2Processor.from_pretrained(
    "USERNAME/quranic-wav2vec2-phonetic"
)
model = Wav2Vec2ForCTC.from_pretrained(
    "USERNAME/quranic-wav2vec2-phonetic"
)

model.eval()

Phonetic Transcription Example

audio, sr = sf.read("recitation.wav")
# convert to mono
if audio.ndim > 1:
    audio = audio.mean(axis=1)

# resample to 16kHz
if sr != 16000:
    audio = librosa.resample(audio, orig_sr=sr, target_sr=16000)

inputs = processor(
    audio,
    sampling_rate=16000,
    return_tensors="pt",
    padding=True,
)

with torch.inference_mode():
    logits = model(inputs.input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)

phonetics = processor.batch_decode(
    predicted_ids,
    skip_special_tokens=True
)[0]

print(phonetics)

Example output:

tālik

TBOGamer22/wav2vec2-quran-phonetics

Quranic Wav2Vec2 Phonetic ASR

Model Description