Instructions to use GENG/hubert_ls_4500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GENG/hubert_ls_4500 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="GENG/hubert_ls_4500")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("GENG/hubert_ls_4500") model = AutoModelForCTC.from_pretrained("GENG/hubert_ls_4500", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from GENG/hubert_ls_4500: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/GENG/hubert_ls_4500/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://GENG/hubert_ls_4500/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/GENG/hubert_ls_4500/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 7dfcf649a169bed6753338ac10ab65ed68406191b8483d638fe282b1d8865f9e
- Size of remote file:
- 378 MB
- SHA256:
- 1e706675f5db487fc58593b1120933caba0f46872e89ee10d06058fb19aba007
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.