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---
language:
- ar
license: apache-2.0
base_model: openai/whisper-medium
tags:
- fine-tuned
- Quran
- automatic-speech-recognition
- arabic
- whisper
datasets:
- fawzanaramam/the-amma-juz
model-index:
- name: Whisper Medium Finetuned on Amma Juz of Quran
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
name: The Amma Juz Dataset
type: fawzanaramam/the-amma-juz
metrics:
- type: eval_loss
value: 0.0032
- type: eval_wer
value: 0.5102
---
# Whisper Medium Finetuned on Amma Juz of Quran
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium), tailored for transcribing Arabic audio with a focus on Quranic recitation from the *Amma Juz* dataset. It is optimized for high accuracy and minimal word error rates in Quranic transcription tasks.
## Model Description
Whisper Medium is a transformer-based automatic speech recognition (ASR) model developed by OpenAI. This fine-tuned version leverages the *Amma Juz* dataset to enhance performance in recognizing Quranic recitations. The model is particularly effective for Arabic speech transcription in religious contexts, while retaining Whisper's general-purpose ASR capabilities.
## Performance Metrics
On the evaluation set, the model achieved:
- **Evaluation Loss**: 0.0032
- **Word Error Rate (WER)**: 0.5102%
- **Evaluation Runtime**: 47.9061 seconds
- **Evaluation Samples per Second**: 2.087
- **Evaluation Steps per Second**: 0.271
These metrics demonstrate the model's superior accuracy and efficiency, making it suitable for applications requiring high-quality Quranic transcription.
## Intended Uses & Limitations
### Intended Uses
- **Speech-to-text transcription** of Quranic recitation in Arabic, specifically from the *Amma Juz*.
- Research and development of tools for Quranic education and learning.
- Projects focused on Arabic ASR in religious and educational domains.
### Limitations
- The model is fine-tuned on Quranic recitations and may not generalize well to non-Quranic Arabic speech or casual conversations.
- Variations in recitation style, audio quality, or heavy accents may impact transcription accuracy.
- For optimal performance, use clean and high-quality audio inputs.
## Training and Evaluation Data
The model was trained using the *Amma Juz* dataset, which includes Quranic audio recordings and corresponding transcripts. The dataset was carefully curated to ensure the integrity and accuracy of Quranic content.
## Training Procedure
### Training Hyperparameters
The following hyperparameters were used during training:
- **Learning Rate**: 1e-05
- **Training Batch Size**: 16
- **Evaluation Batch Size**: 8
- **Seed**: 42
- **Optimizer**: Adam (betas=(0.9, 0.999), epsilon=1e-08)
- **Learning Rate Scheduler**: Linear
- **Warmup Steps**: 10
- **Number of Epochs**: 3.0
- **Mixed Precision Training**: Native AMP
### Framework Versions
- **Transformers**: 4.41.1
- **PyTorch**: 2.2.1+cu121
- **Datasets**: 2.19.1
- **Tokenizers**: 0.19.1 |