TuniSpeech-AI/whisper-tunisian-dialect

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)

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}
}