Instructions to use SPRINGLab/SPRING_F5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SPRINGLab/SPRING_F5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="SPRINGLab/SPRING_F5", trust_remote_code=True)# Load model directly from transformers import SPRING_F5 model = SPRING_F5.from_pretrained("SPRINGLab/SPRING_F5", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb09957d1a29ca70bcaf86f6c53a6014f47c9d59d9b9102c3c22e57ac3957472
- Size of remote file:
- 256 kB
- SHA256:
- b0e22048e72414fcc1e6b6342e47a774d748a195ed34e4a5b3fcf416707f2b71
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