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:
- f68aa295a7589b409aef4b3dbfdc9818f3ab9f527e2bd2a9633f058f32de7747
- Size of remote file:
- 180 kB
- SHA256:
- bb15708b4b3875e37beec46591a5d89e1a9a63fdad3b8fe4a5c8738f4f554400
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