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Deep learning for Arabic text Vocalization - التشكيل الالي للنصوص العربية
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dictionary beta version 0.1 Dec 20, 2017
images shakkala version 2 enhancments Jun 18, 2018
model shakkala version 2 enhancments Jun 18, 2018
requirements Jinja2 update to recommended Apr 13, 2019
LICENSE.md files and read me Jan 8, 2018
README.md add more details to references Jun 5, 2019
Shakkala.py Fix indentation conflicts Feb 21, 2019
demo.py Fix indentation conflicts Feb 21, 2019
helper.py Fix indentation conflicts Feb 21, 2019

README.md

Shakkala Project V 2.0 مشروع شكّالة

Model

Introduction

Shakkala project use recurrent neural network for Arabic text vocalization to automatically form Arabic characters (تشكيل الحروف) which can be used to enhance text-to-speech systems.
This model can be used in many applications such as enhance text-to-speech systems or search results.

Requirements

Execute following commands:

cd requirements
pip install -r requirements.txt

Code Examples (How to)

Check full example in (demo.py) file.

  1. Create Shakkala object
sh = Shakkala(folder_location, version={version_num})
  1. Prepare input
input_int = sh.prepare_input(input_text)
  1. Call the neural network
model, graph = sh.get_model()
with graph.as_default():
  logits = model.predict(input_int)[0]
  1. Predict output
predicted_harakat = sh.logits_to_text(logits)
final_output = sh.get_final_text(input_text, predicted_harakat)

Available models:

  • version_num=1: First test of the solution.
  • version_num=2: Main release version.
  • version_num=3: Some enhancements from version number 2.

It worth to try both version_num=2 and version_num=3.

Perfomance Tips

Shakkala built in object oriented way to load the model once into memory for faster prediction, to make sure you dont load it multiple times in your service or application follow the steps:

  • Load the model in global variable:
sh = Shakkala(folder_location, version={version_num})
model, graph = sh.get_model()
  • Then inside your request function or loop add:
input_int = sh.prepare_input(input_text)
with graph.as_default():
  logits = model.predict(input_int)[0]
predicted_harakat = sh.logits_to_text(logits)
final_output = sh.get_final_text(input_text, predicted_harakat)

Accuracy

In this beta version 2 accuracy reached up to 95% and in some data it reach more based on complexity and data disribution. This beta version trained on more than million sentences with majority of historical Arabic data from books and some of available formed modern data in the internet.

history

Prediction Example

For live demo based on Shakkala library click the link

Real output Predicted output
فَإِنْ لَمْ يَكُونَا كَذَلِكَ أَتَى بِمَا يَقْتَضِيهِ الْحَالُ وَهَذَا أَوْلَى فَإِنْ لَمْ يَكُونَا كَذَلِكَ أَتَى بِمَا يَقْتَضِيهِ الْحَالُ وَهَذَا أَوْلَى
قَالَ الْإِسْنَوِيُّ وَسَوَاءٌ فِيمَا قَالُوهُ مَاتَ فِي حَيَاةِ أَبَوَيْهِ أَمْ لَا قَالَ الْإِسْنَوِيُّ وَسَوَاءٌ فِيمَا قَالُوهُ مَاتَ فِي حَيَاةِ أَبَوَيْهِ أَمْ لَا
طَابِعَةٌ ثُلَاثِيَّةُ الْأَبْعَاد طَابِعَةٌ ثَلَاثِيَّةُ الْأَبْعَادِ

Accuracy Enhancements

The model can be enhanced to reach more than 95% accuracy with following:

  • Availability of more formed modern data to train the network. (because current version trained with mostly available historical Arabic data and some modern data)
  • Stack different models

Model Design

Model

References

Citation

For academic work use

Shakkala, Arabic text vocalization, Barqawi & Zerrouki

OR bibtex format

@misc{
  title={Shakkala, Arabic text vocalization},
  author={Barqawi, Zerrouki},
  url={https://github.com/Barqawiz/Shakkala},
  year={2017}
}

Contribution

Core Team

  1. Ahmad Barqawi: Neural Network Developer.
  2. Taha Zerrouki: Mentor Data and Results.

Contributors

  1. Zaid Farekh & propellerinc.me: Provide infrastructure and consultation support.
  2. Mohammad Issam Aklik: Artist.
  3. Brahim Sidi: Form new sentences.
  4. Fadi Bakoura: Aggregate online content.
  5. Ola Ghanem: Testing.
  6. Ali Hamdi Ali Fadel: Contribute code.

License

Free to use and distribute only mention the original project name Shakkala as base model.

The MIT License (MIT)

Copyright (c) 2017 Shakkala Project

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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