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dyu-mms-tts
Dioula text-to-speech: MMS-TTS (VITS) fine-tuned by GO AI Corp.
| Task | Text-to-speech, Dioula (dyu) |
| Base model | facebook/mms-tts |
| Training data | Not recorded |
| Published | May 2026 |
Résumé en français. Synthèse vocale en dioula : MMS-TTS (VITS) affiné par GO AI Corp à partir de
facebook/mms-tts. Données d'entraînement non notées. Publié en mai 2026. Licence CC BY-NC 4.0 ; tout usage commercial demande l'autorisation écrite de contact@goaicorporation.org. Accès sur demande.
Quick start
import torch, scipy.io.wavfile
from transformers import VitsModel, AutoTokenizer
name = "goaicorp/dyu-mms-tts"
model, tok = VitsModel.from_pretrained(name), AutoTokenizer.from_pretrained(name)
with torch.no_grad():
wav = model(**tok("I ni ce.", return_tensors="pt")).waveform[0].numpy()
scipy.io.wavfile.write("out.wav", model.config.sampling_rate, wav)
Evaluation
| Test set | CER (ASR loopback) | UTMOS | Speaker similarity |
|---|---|---|---|
| Dioula held-out set (to be fixed) | pending | pending | pending |
CER is measured by transcribing the synthetic speech with a fixed Mooré ASR model.
Limits
- Training data is not documented. The earlier card was titled "Bambara TTS"; the model is tagged and described as Dioula.
- One voice; no voice cloning.
License
CC BY-NC 4.0. Commercial use needs written permission from contact@goaicorporation.org.
Citation
@misc{goaicorp2026dyummstts,
title = {dyu-mms-tts: Dioula text-to-speech with MMS-TTS},
author = {Nyantudre, Wendyellé Abubakrh Alban and Bandaogo, Aristide},
year = {2026},
howpublished = {Hugging Face Models, GO AI Corp},
url = {https://huggingface.co/goaicorp/dyu-mms-tts}
}
Developed and maintained by
- Wendyellé A. Alban NYANTUDRE: nyantudre@gmail.com
- Aristide BANDAOGO: aristide@goaicorporation.org
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