LinTO Audio and Textual Datasets to Train and Evaluate Automatic Speech Recognition in Tunisian Arabic Dialect
Paper • 2504.02604 • Published • 1
SILMA TTS — Tunisian Derja fine-tune (v2, quality-focused)
Fine-tune of silma-ai/silma-tts on the LinTO Tunisian dataset.
v2 recipe: speaker_mode=multi (single≈AbdelAzizErwi), moderate text
normalization, audio-quality filtering (SNR≥8.0dB), phonemization=False,
40 epochs. Targets pronunciation/vowel/consonant quality by improving the signal
rather than the architecture. Deploy raw or EMA weights per the A/B in §8.
Usage
Load with the F5-TTS v1.1.7 / SILMA pipeline: model.pt + vocab.txt + config.yaml.
The vocab.txt here matches this run (SILMA char vocab, or phonemized vocab if piloted).
Attribution (required — CC BY 4.0)
@misc{linagora2024Linto-tn,
title={LinTO Audio and Textual Datasets to Train and Evaluate Automatic Speech Recognition in Tunisian Arabic Dialect},
author={Hedi Naouara and Jerome Louradour and Jean-Pierre Lorre},
year={2025}, eprint={2504.02604}, archivePrefix={arXiv}, primaryClass={cs.CL}
}
Base: SILMA TTS (Apache-2.0). Data: LinTO (CC BY 4.0).
Responsible use
Voice cloning requires documented speaker consent. Do not use for deception or impersonation.
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Base model
silma-ai/silma-tts
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