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:
- a4445443063925692d6e7add75e7f3fbe6306022c96c06ddaaf3034c4644bcb0
- Size of remote file:
- 229 kB
- SHA256:
- e7d069b8ebd5180c3b30fde5d378f0a1ddac96722d62cf43537efc3c3f3a3ce8
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