Instructions to use goaicorp/mos-mms-tts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use goaicorp/mos-mms-tts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="goaicorp/mos-mms-tts")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("goaicorp/mos-mms-tts") model = AutoModelForTextToWaveform.from_pretrained("goaicorp/mos-mms-tts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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mos-mms-tts is a GO AI Corp model shared for non-commercial research (CC BY-NC 4.0). Commercial use needs written permission from contact@goaicorporation.org. Please read the card, its terms and licenses before requesting access, and make sure you understand them.
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mos-mms-tts
Mooré text-to-speech: MMS-TTS (VITS) fine-tuned by GO AI Corp.
| Task | Text-to-speech, Mooré (mos) |
| Base model | MMS-TTS (VITS); the starting checkpoint was not recorded, most likely facebook/mms-tts-mos |
| Training data | Not recorded |
| Published | July 2024 (updated August 2024) |
| Related | Served by the product endpoint mos-tts |
Résumé en français. Synthèse vocale en mooré : MMS-TTS (VITS) affiné par GO AI Corp. Le point de départ n'a pas été noté (très probablement
facebook/mms-tts-mos), pas plus que les données d'entraînement. Publié en juillet 2024 (mis à jour en août 2024) ; c'est le modèle servi par le point d'accèsmos-tts. 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/mos-mms-tts"
model, tok = VitsModel.from_pretrained(name), AutoTokenizer.from_pretrained(name)
with torch.no_grad():
wav = model(**tok("Ne y yibeoogo.", 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 |
|---|---|---|---|
| Mooré 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.
- One voice; no voice cloning.
License
CC BY-NC 4.0. Commercial use needs written permission from contact@goaicorporation.org.
Citation
@misc{goaicorp2024mosmmstts,
title = {mos-mms-tts: Mooré text-to-speech with MMS-TTS},
author = {Nyantudre, Wendyellé Abubakrh Alban and Bandaogo, Aristide},
year = {2024},
howpublished = {Hugging Face Models, GO AI Corp},
url = {https://huggingface.co/goaicorp/mos-mms-tts}
}
Developed and maintained by
- Wendyellé A. Alban NYANTUDRE: nyantudre@gmail.com
- Aristide BANDAOGO: aristide@goaicorporation.org
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