Whisper Tunisian Dialect ASR (TuniSpeech‑21h)
Whisper Tunisian Dialect ASR
Overview
Whisper Tunisian Dialect ASR is a fine-tuned speech recognition model based on OpenAI Whisper-large-v2, adapted to the Tunisian Arabic dialect using the TuniSpeech corpus (~21 hours).
The model is designed for:
- Automatic Speech Recognition (ASR) in Tunisian dialect
- Transcription of short spontaneous speech
- Research on low-resource Arabic dialects
Repository:
https://huggingface.co/TuniSpeech-AI/whisper-tunisian-dialect
Model Architecture
- Base model: Whisper-large-v2
- Fine-tuning strategy: LoRA → merged into full model weights
- Framework: Hugging Face Transformers
- Decoding:
- Forced language: Arabic (
ar) - Task: transcription (not translation)
- Forced language: Arabic (
After training, LoRA adapters were fully merged, producing a standalone Whisper checkpoint compatible with standard inference pipelines.
Training Data
TuniSpeech Corpus
- Language: Tunisian Arabic dialect
- Duration: ~21 hours
- Recording conditions:
- Real speech\
- Spontaneous pronunciation\
- Speaker variability
The dataset targets dialectal phonetics and vocabulary, which are poorly covered by standard Arabic ASR systems.
Experimental Setup
- Input audio:
- Resampled to 16 kHz mono
- Training method:
- Parameter-efficient fine-tuning (LoRA)
- Followed by weight merging
Evaluation focused on:
- Dialectal word recognition
- Short utterance transcription
- Real-world speech robustness
Usage
Installation
pip install torch transformers librosa gradio
Python Inference
from transformers import pipeline
pipe = pipeline(
"automatic-speech-recognition",
model="TuniSpeech-AI/whisper-tunisian-dialect"
)
result = pipe("audio.wav")
print(result["text"])
Hugging Face Space
An interactive demo is available:
Upload or record Tunisian speech → get transcription instantly.
⚠️ On CPU, processing is slow.
Recommended:
- Use short audio (< 5 s)
Citation
If you use this model, please cite the associated work on:
Whisper fine-tuning for Tunisian dialect speech recognition using the TuniSpeech-21H corpus.
@inproceedings{tunispeech_whisper_2026,
title = {A New Tunisian Arabic Corpus and Benchmark for Automatic Speech Recognition},
author = {Sghaier, Mohamed Ali and Bellagha, Mohamed Lazhar and Zrigui, Mounir},
booktitle = {In Proceedings of the 18th International Conference on Agents and Artificial Intelligence},
year = {2026}
}
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