Instructions to use jaketae/fastspeech2-ljspeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaketae/fastspeech2-ljspeech with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jaketae/fastspeech2-ljspeech", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f13a85a55981b5166344d62eed3f46f91c787a627b70f5161e9a7ee27e983434
- Size of remote file:
- 166 MB
- SHA256:
- 513573603ef1d07211bef95c231edd57cf417915992b45c451c2eb1a442aa6ff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.