metadata
language:
- wo
license: cc-by-nc-4.0
library_name: coqui-tts
tags:
- text-to-speech
- tts
- vits
- wolof
- senegal
- low-resource
- african-languages
datasets:
- AIHubSN/Sunubaat-Wolof-TTS
pipeline_tag: text-to-speech
WolofTTS (VITS)
Single-speaker VITS text-to-speech checkpoint for Wolof, trained by AI Hub Senegal on the Sunubaat Wolof TTS corpus with 🐸 Coqui TTS. VITS is end-to-end, so model.pth + config.json are all you need — no separate vocoder.
Trained on ~20 hours / 9,183 utterances of a single native (female) Wolof speaker, covering agriculture, fishing, health, education, news, sports, politics, and daily life — see the dataset card for details. Licensed CC BY-NC 4.0, matching the training data: non-commercial use with attribution.
Install
pip install TTS
Usage
Download model.pth and config.json into the same folder, then:
from TTS.utils.synthesizer import Synthesizer
synthesizer = Synthesizer(
tts_checkpoint="model.pth",
tts_config_path="config.json",
)
wav = synthesizer.tts(text="salamalekum, na nga def")
synthesizer.save_wav(wav, "output.wav")
Or via the CLI:
tts --model_path model.pth \
--config_path config.json \
--text "salamalekum, na nga def" \
--out_path output.wav
Notes
- Input text should be lower-cased (the model was trained on lower-cased graphemes, no phonemizer).
- Standalone digits (
0–10) are commonly converted to French number words (e.g."3"→"trois") before synthesis, reflecting Wolof/French code-switching — do this yourself if you want matching pronunciation. - Single speaker only — no
speaker_wav/ speaker id arguments apply.