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
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Download f5_tts/model/backbones/README.md from SPRINGLab/SPRING_F5: direct link, hf CLI and curl.
- Browser
- Download file 708 Bytes
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https://huggingface.co/SPRINGLab/SPRING_F5/resolve/main/f5_tts/model/backbones/README.md
- Command line
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hf download hf://SPRINGLab/SPRING_F5/f5_tts/model/backbones/README.md
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curl -L -o README.md https://huggingface.co/SPRINGLab/SPRING_F5/resolve/main/f5_tts/model/backbones/README.md
708 Bytes
Backbones quick introduction
unett.py
- flat unet transformer
- structure same as in e2-tts & voicebox paper except using rotary pos emb
- possible abs pos emb & convnextv2 blocks for embedded text before concat
dit.py
- adaln-zero dit
- embedded timestep as condition
- concatted noised_input + masked_cond + embedded_text, linear proj in
- possible abs pos emb & convnextv2 blocks for embedded text before concat
- possible long skip connection (first layer to last layer)
mmdit.py
- stable diffusion 3 block structure
- timestep as condition
- left stream: text embedded and applied a abs pos emb
- right stream: masked_cond & noised_input concatted and with same conv pos emb as unett