Text-to-Speech
Transformers
tts
voice-cloning
yoruba
hausa
igbo
pidgin
nigerian-english
african-languages
Instructions to use Axiveri/WazobiaVoice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Axiveri/WazobiaVoice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Axiveri/WazobiaVoice")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Axiveri/WazobiaVoice", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ff2de3c4ebdd86078a333b75f8f9030f97881e244e1b7573d50e7f33c9a894c
- Size of remote file:
- 622 kB
- SHA256:
- b6db06ad58744099e67630561e3d15f92efa3f92dce15dfeac14a60273b1e5d3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.