Instructions to use susnato/clvp_dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use susnato/clvp_dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="susnato/clvp_dev")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("susnato/clvp_dev") model = AutoModel.from_pretrained("susnato/clvp_dev") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
DISCLAIMER : I do not own any weights present in this repository. All weights belong to the author of the
paper - "Better speech synthesis through scaling", James Betker . I am storing the weights(temporarily) for the tortoise-tts integration
to Huggingface. Please refer to this PR to know more.
About
CLVP model is an integral part of tortoise-tts presented in the paper - "Better speech synthesis through scaling" by James Betker.
CLVP uses an architecture similar to the CLIP text encoder, except it uses two of them: one for text
tokens and the other for MEL tokens.
- Downloads last month
- 90,914