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:
- 76419fae6ca4ab24f57a088ad8b8a2ea814025ad02035687d26599cc61059658
- Size of remote file:
- 700 kB
- SHA256:
- 4c0996e4e6847f714e5b3a460f12ab650f34dda40ab825a304fcb5d0fc36da7d
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