Instructions to use adbcode/speecht5_voxpopuli_sl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adbcode/speecht5_voxpopuli_sl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="adbcode/speecht5_voxpopuli_sl")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("adbcode/speecht5_voxpopuli_sl") model = AutoModelForTextToSpectrogram.from_pretrained("adbcode/speecht5_voxpopuli_sl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from adbcode/speecht5_voxpopuli_sl: direct link, hf CLI and curl.
- Browser
- Download file 578 MB
-
https://huggingface.co/adbcode/speecht5_voxpopuli_sl/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://adbcode/speecht5_voxpopuli_sl/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/adbcode/speecht5_voxpopuli_sl/resolve/main/pytorch_model.bin
578 MB
- Xet hash:
- 2add1069cb35a636bbf15273bab3386b33fc8d73235c7ae9d5c65e9af1889bdb
- Size of remote file:
- 578 MB
- SHA256:
- d1d644a58c57ad9ac311808a29c824f71ecd4a670405c841dd96a56048d7349f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.