Quranic Wav2Vec2 Phonetic ASR
Model Description
Quranic Wav2Vec2 Phonetic ASR is a fine-tuned wav2vec2 model designed specifically for phonetic transcription of Quranic recitation.
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
Audiofeature - 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
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