Instructions to use Nuwaisir/Quran_speech_recognizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nuwaisir/Quran_speech_recognizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Nuwaisir/Quran_speech_recognizer")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Nuwaisir/Quran_speech_recognizer") model = AutoModelForCTC.from_pretrained("Nuwaisir/Quran_speech_recognizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Nuwaisir Rabi commited on
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# Quran Speech Recognizer
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This application will listen to the user's Quran recitation, and take the
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user to the position of the Quran from where the s/he had recited.
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You can also take a look at our [presentation slides](https://docs.google.com/presentation/d/1dbbVYHi3LQRiggH14nN36YV2A-ddUAKg67aX5MWi0ys/edit?usp=sharing).
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# Methodology
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We used transfer learning to make our application. We fine-tuned the pretrained
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model available at https://huggingface.co/elgeish/wav2vec2-large-xlsr-53-arabic
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using the data available at https://www.kaggle.com/c/quran-asr-challenge/data.
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Our model can be found at https://huggingface.co/Nuwaisir/Quran_speech_recognizer.
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# Usage
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Run all the cells of Notebooks/run_ui.ipynb. The last cell will hear your
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recitation for 5 seconds (changeable) from the time you run that cell. And then convert your
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speech to Arabic text and show the most probable corresponding parts of 30th juzz
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(Surah 78 - 114) of the Quran as the output based on edit distance value.
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Currently, we are searching from Surah 78 to Surah 114 as the searching
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algorithm needs some time to search the whole Quran. This range can be changed
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in the 6th cell of the notebook.
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