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)# pip install -U transformers accelerate # 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
Download f5_tts/model/__init__.py from SPRINGLab/SPRING_F5: direct link, hf CLI and curl.
- Browser
- Download file 267 Bytes
-
https://huggingface.co/SPRINGLab/SPRING_F5/resolve/main/f5_tts/model/__init__.py
- Command line
-
hf download hf://SPRINGLab/SPRING_F5/f5_tts/model/__init__.py
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curl -L -o __init__.py https://huggingface.co/SPRINGLab/SPRING_F5/resolve/main/f5_tts/model/__init__.py
267 Bytes
| from f5_tts.model.backbones.dit import DiT | |
| from f5_tts.model.backbones.mmdit import MMDiT | |
| from f5_tts.model.backbones.unett import UNetT | |
| from f5_tts.model.cfm import CFM | |
| from f5_tts.model.trainer import Trainer | |
| __all__ = ["CFM", "UNetT", "DiT", "MMDiT", "Trainer"] | |