Tunisian_STT

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🇹🇳 Tunisian STT — Automatic Speech Recognition

Tunisian STT is a high-quality Automatic Speech Recognition (ASR) system dedicated to the Tunisian Arabic dialect. The model is designed for real-world usage and supports code-switching, enabling robust recognition of Tunisian Arabic mixed with French and English terms.

This repository provides production-ready speech-to-text models along with a language model (ARPA format), making it suitable for research, prototyping, and deployment across multiple platforms.

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✨ Key Features

• 🎙️ Native support for Tunisian Arabic dialect
• 🌍 Robust handling of code-switching (Arabic–French–English)
• ⚡ Optimized variants for mobile and embedded devices
• 🧠 Powered by Kaldi TDNN architecture
• 📦 Ready-to-use with Vosk

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📦 Available Models

This repository includes two acoustic model variants:

• STT_Tun_Model The full-scale, high-accuracy model suitable for servers and desktop environments.

• android-model A lightweight version with a simplified decoding graph, optimized for Android, Raspberry Pi, and edge devices.

Both models are shipped with a language model (ARPA) for decoding.

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🧠 Model Overview

• Model Type: TDNN (Time-Delay Neural Network)
• Framework: Kaldi
• Primary Language: Tunisian Arabic
• Task: Automatic Speech Recognition (ASR)

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📊 Performance Benchmarks

The table below reports Character Error Rate (CER) and Word Error Rate (WER) across multiple Tunisian speech datasets:

Note: Performance varies depending on recording quality, speaker accent, and code-switching density.

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🏗️ Training Pipeline

The model was trained using the official Kaldi-based training pipeline available here:

➡️ Training Code: ASR_train_kaldi_tunisian

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🗂️ Training Datasets

The acoustic model was trained on a diverse collection of Tunisian Arabic speech datasets:

• TARIC Real-world speech recordings collected in Tunisian train stations.

• LinTO Dataset – Audio for Tunisian Arabic A curated corpus of annotated Tunisian dialect speech sourced from Hugging Face, YouTube, and web data.

• LinTO Dataset – Augmented Version An enhanced dataset with data augmentation techniques including noise robustness and voice conversion.

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🚀 Getting Started

1️⃣ Download the Model

Option A — Hugging Face Website

1. Visit the model page: 👉 https://huggingface.co/Sali7a8603/Tunisian_STT
2. Click Download ZIP to retrieve the desired model.

Option B — Command Line (Recommended)

sudo apt-get install curl

curl -L https://huggingface.co/Sali7a8603/Tunisian_STT/resolve/main/STT_Tun_Model.zip \
     --output STT_Tun_Model.zip

Replace STT_Tun_Model.zip with android-model.zip if needed.

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2️⃣ Extract the Model

mkdir tunisian_stt_model
unzip STT_Tun_Model.zip -d tunisian_stt_model

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3️⃣ Run Test (Python)

Install Dependencies

pip install vosk

Run Speech-to-Text

python testing.py <path/to/model> <path/to/audio.wav>

The output will be the transcribed text in Tunisian Arabic, with support for embedded French and English terms.

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📜 License

This project is released under the Apache 2.0 License, allowing both academic and commercial usage.

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🤝 Contributions & Feedback

Contributions, issues, and feature requests are welcome. If you use this model in your research or product, a citation or acknowledgment would be appreciated.