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:
- 4901f683ad008b66e7f5de277bf44f9430991db762ef45cc1c9e6b22e43845f2
- Size of remote file:
- 300 kB
- SHA256:
- 37e5be63809d5b19715ca6fa06d2aaf9d1c9f2ac21b59308ca98f8ddb545b17c
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