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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| **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](https://github.com/huggingface/transformers/pull/24745) to know more. | |
| <h3><u>About</u></h3> | |
| 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. |