Instructions to use OpenSound/CapSpeech-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenSound/CapSpeech-models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="OpenSound/CapSpeech-models")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenSound/CapSpeech-models", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e3c862199121e0d8116818276be09040cf774599718f61c84958e1c2bfcc3f8
- Size of remote file:
- 7.37 GB
- SHA256:
- 382c47ece1345fe9528e8579fc307ba7d9bd03c6978c25ced8f79998e4a09592
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.