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:
- 7aad8588a0150bc4e3ec80ae7afec7a41107e2e428b92b4ed5d1e62ca1a1c957
- Size of remote file:
- 5.4 GB
- SHA256:
- 6824673c8b5e570b2f5ef231e19be2f9e756cfccddee63650ed96f77510092ba
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