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:
- b6f64f39a5ef8272e01fec1db37be446a56e0aab57215b7491403b8efb6caefa
- Size of remote file:
- 322 kB
- SHA256:
- 466293886a22abe65727510640bc3f16bae545e532e807371cb995037a86d79e
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