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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Gatozu35/tortoise-tts") model = AutoModel.from_pretrained("Gatozu35/tortoise-tts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Gatozu35/tortoise-tts: direct link, hf CLI and curl.
- Browser
- Download file 1.61 GB
-
https://huggingface.co/Gatozu35/tortoise-tts/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Gatozu35/tortoise-tts/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Gatozu35/tortoise-tts/resolve/main/pytorch_model.bin
1.61 GB
- Xet hash:
- 590991ac5504389acec60674b574a7dc9b492e224755f3f3a73d73a75fb2834b
- Size of remote file:
- 1.61 GB
- SHA256:
- d99e31f843917647a54f95bb25a86b2bf62288f42e20c528899b7b39fa0c2f9d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.