Text-to-Speech
Transformers
Safetensors
Chinese
English
audio
speech-recognition
voice-cloning
speech-editing
comfyui
Instructions to use t8star/Firered-Audio-Comfy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use t8star/Firered-Audio-Comfy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="t8star/Firered-Audio-Comfy")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("t8star/Firered-Audio-Comfy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download RedAE_decoder/model.pt from t8star/Firered-Audio-Comfy: direct link, hf CLI and curl.
- Browser
- Download file 8.4 GB
-
https://huggingface.co/t8star/Firered-Audio-Comfy/resolve/main/RedAE_decoder/model.pt
- Command line
-
hf download hf://t8star/Firered-Audio-Comfy/RedAE_decoder/model.pt
-
curl -L -o model.pt https://huggingface.co/t8star/Firered-Audio-Comfy/resolve/main/RedAE_decoder/model.pt
8.4 GB
- Xet hash:
- cc18be1e2a0f30eab0927e6cf96480c18b04a711c23d71fab9a8d0ece4790b83
- Size of remote file:
- 8.4 GB
- SHA256:
- 020b4d5eb7bcf3dbca1133023f6a62366cc213491bbb5f334b8691e2949e0152
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.