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--- |
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language: |
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- ar |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- fine-tuned |
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- Quran |
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- automatic-speech-recognition |
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- arabic |
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- whisper |
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datasets: |
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- fawzanaramam/the-amma-juz |
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model-index: |
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- name: Whisper small Finetuned on Amma Juz of Quran |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Speech Recognition |
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dataset: |
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name: The Amma Juz Dataset |
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type: fawzanaramam/the-amma-juz |
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metrics: |
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- type: eval_loss |
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value: 0.0058 |
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- type: eval_wer |
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value: 1.1494 |
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--- |
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# Whisper Small Finetuned on Amma Juz of Quran |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small), specialized in transcribing Arabic audio with a focus on Quranic recitation from the *Amma Juz* dataset. This fine-tuning makes the model highly effective for tasks involving accurate recognition of Arabic speech, especially in religious and Quranic contexts. |
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## Model Description |
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Whisper Small is a transformer-based model for automatic speech recognition (ASR), developed by OpenAI. By fine-tuning it on the *Amma Juz* dataset, this version achieves state-of-the-art results on transcribing Quranic recitations with minimal word error rates and high accuracy. The fine-tuned model retains the original capabilities of the Whisper architecture while being optimized for Arabic Quranic text. |
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## Performance Metrics |
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On the evaluation set, the model achieved: |
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- **Evaluation Loss**: 0.0058 |
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- **Word Error Rate (WER)**: 1.1494% |
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- **Evaluation Runtime**: 44.2766 seconds |
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- **Evaluation Samples per Second**: 2.259 |
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- **Evaluation Steps per Second**: 0.294 |
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These metrics demonstrate the model's efficiency and accuracy when processing Quranic recitations. |
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## Intended Uses & Limitations |
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### Intended Uses |
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- **Speech-to-text transcription** of Arabic Quranic recitation, specifically from the *Amma Juz*. |
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- Research and educational purposes in the domain of Quranic studies. |
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- Applications in tools for learning Quranic recitation. |
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### Limitations |
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- The model is fine-tuned on Quranic recitation and may not perform as well on non-Quranic Arabic speech or general Arabic conversations. |
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- Noise in audio inputs, variations in recitation style, or heavy accents might affect accuracy. |
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- It is recommended to use clean and high-quality audio for optimal performance. |
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## Training and Evaluation Data |
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The model was trained using the *Amma Juz* dataset, which comprises Quranic audio data and corresponding transcripts. This dataset was curated to ensure high-quality representation of Quranic recitations. |
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## Training Procedure |
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### Training Hyperparameters |
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The following hyperparameters were used during training: |
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- **Learning Rate**: 1e-05 |
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- **Training Batch Size**: 16 |
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- **Evaluation Batch Size**: 8 |
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- **Seed**: 42 |
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- **Optimizer**: Adam (betas=(0.9, 0.999), epsilon=1e-08) |
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- **Learning Rate Scheduler**: Linear |
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- **Warmup Steps**: 10 |
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- **Number of Epochs**: 3.0 |
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- **Mixed Precision Training**: Native AMP |
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### Framework Versions |
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- **Transformers**: 4.41.1 |
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- **PyTorch**: 2.2.1+cu121 |
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- **Datasets**: 2.19.1 |
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- **Tokenizers**: 0.19.1 |
