Instructions to use Gatozu35/tortoise-tts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gatozu35/tortoise-tts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Gatozu35/tortoise-tts")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Gatozu35/tortoise-tts") model = AutoModel.from_pretrained("Gatozu35/tortoise-tts") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6f888ca8be4adcdb293cc35e4f4b3072bda373c6765ee45c06673ac8420116c
- Size of remote file:
- 1.17 GB
- SHA256:
- da0582f741644405091e1c7869a45d5191d3d981a2cdbefc57b6198ec80247a6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.