---
license: apache-2.0
datasets:
- tarteel-ai/everyayah
- MohamedRashad/Quran-Recitations
language:
- ar
metrics:
- wer
base_model:
- openai/whisper-medium
pipeline_tag: automatic-speech-recognition
library_name: transformers
tags:
- Quran
- Tajweed
- Recitation
- Islam
- Arabic
- whisper
---

# Whisper Medium Quran (LoRA Fine-Tuned)

This is a specialized **Automatic Speech Recognition (ASR)** model for **Quranic Recitation** with **tashkeel** or **diacritics**. It is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium), optimized to recognize Quranic Arabic with high accuracy while maintaining robustness across different recording conditions.

## Model Performance
- **Word Error Rate (WER):** Achieved **12.69%** on the `tarteel-ai/everyayah` validation set.
- **Accuracy:** The model demonstrates high precision in capturing Quranic vocabulary and Uthmani script nuances.

## Training Details
The model was trained using **LoRA (Low-Rank Adaptation)** in a multi-stage curriculum learning process to ensure stability and precision.

### Datasets
The training utilized a mix of professional and diverse recitations from two primary sources:
1.  **[MohamedRashad/Quran-Recitations](https://huggingface.co/datasets/MohamedRashad/Quran-Recitations)**
2.  **[tarteel-ai/everyayah](https://huggingface.co/datasets/tarteel-ai/everyayah)** (Highly diverse professional recitations)

### Methodology
- **Curriculum Learning:** The model was trained gradually across these datasets to refine its understanding of Tajweed and Quranic sentence structures.
- **Data Augmentation:** To ensure the model remains robust against real-world conditions (non-studio microphones, background noise, varying volumes), diverse audio augmentations including gain adjustments and spectral masking were applied during the training process.

## Usage

This model is fully compatible with the Hugging Face `transformers` pipeline. For longer verses, chunking is recommended to maintain context.

```python
from transformers import pipeline

# Load the pipeline
pipe = pipeline(
    "automatic-speech-recognition",
    model="MaddoggProduction/whisper-m-quran-lora-dataset-mix",
    device=0 # for GPU usage, -1 for CPU
)

# Transcribe audio (chunking enabled for long verses)
result = pipe(
    "path_to_audio.mp3",
    chunk_length_s=30, # Critical for long verses like 2:282, to avoid hallucinations
    stride_length_s=5,
    batch_size=8,
    return_timestamps=True
)

print(result["text"])