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:
- 41cd2907a34cf9b7c7c3d203e62e689b5b3aa88011b5d8e72f8f3a8dad6666a8
- Size of remote file:
- 673 kB
- SHA256:
- 3d7d5dd243c859a37d69dfa0c4de6dadfad959c0534fdd27c49e7c2c60fe61ae
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