Automatic Speech Recognition
Transformers
Safetensors
Arabic
whisper
Quran
Tajweed
Recitation
Islam
Arabic
Instructions to use MaddoggProduction/whisper-m-quran-lora-dataset-mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaddoggProduction/whisper-m-quran-lora-dataset-mix with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MaddoggProduction/whisper-m-quran-lora-dataset-mix")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("MaddoggProduction/whisper-m-quran-lora-dataset-mix") model = AutoModelForSpeechSeq2Seq.from_pretrained("MaddoggProduction/whisper-m-quran-lora-dataset-mix", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Whisper Medium Quran (LoRA Fine-Tuned)
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This is a specialized **Automatic Speech Recognition (ASR)** model for **Quranic Recitation**. 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.
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## Model Performance
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- **Word Error Rate (WER):** Achieved **12.69%** on the `tarteel-ai/everyayah` validation set.
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# Whisper Medium Quran (LoRA Fine-Tuned)
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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.
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## Model Performance
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- **Word Error Rate (WER):** Achieved **12.69%** on the `tarteel-ai/everyayah` validation set.
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