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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-medium |
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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 Medium 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: 0.0032 |
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- type: eval_wer |
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value: 0.5102 |
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--- |
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# Whisper Medium Finetuned on Amma Juz of Quran |
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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. |
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## Model Description |
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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. |
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## Performance Metrics |
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On the evaluation set, the model achieved: |
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- **Evaluation Loss**: 0.0032 |
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- **Word Error Rate (WER)**: 0.5102% |
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- **Evaluation Runtime**: 47.9061 seconds |
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- **Evaluation Samples per Second**: 2.087 |
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- **Evaluation Steps per Second**: 0.271 |
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These metrics demonstrate the model's superior accuracy and efficiency, making it suitable for applications requiring high-quality Quranic transcription. |
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## Intended Uses & Limitations |
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### Intended Uses |
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- **Speech-to-text transcription** of Quranic recitation in Arabic, specifically from the *Amma Juz*. |
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- Research and development of tools for Quranic education and learning. |
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- Projects focused on Arabic ASR in religious and educational domains. |
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### Limitations |
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- The model is fine-tuned on Quranic recitations and may not generalize well to non-Quranic Arabic speech or casual conversations. |
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- Variations in recitation style, audio quality, or heavy accents may impact transcription accuracy. |
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- For optimal performance, use clean and high-quality audio inputs. |
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## Training and Evaluation Data |
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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. |
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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 |
