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:
- a2f6883933b4dfdbc0b32591032ae61ffc5a5462a1dc86acaeb9c666c5bf24aa
- Size of remote file:
- 543 kB
- SHA256:
- fad89833c58c56c85006c241f2684067b90e1963a03e1f28b5db66d2b1a6b80f
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