Instructions to use GENG/hubert_ls_2500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GENG/hubert_ls_2500 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="GENG/hubert_ls_2500")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("GENG/hubert_ls_2500") model = AutoModelForCTC.from_pretrained("GENG/hubert_ls_2500", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from GENG/hubert_ls_2500: direct link, hf CLI and curl.
- Browser
- Download file 722 MB
-
https://huggingface.co/GENG/hubert_ls_2500/resolve/main/optimizer.pt
- Command line
-
hf download hf://GENG/hubert_ls_2500/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/GENG/hubert_ls_2500/resolve/main/optimizer.pt
722 MB
- Xet hash:
- 43917e6b17da2025073871feba0cb1de018b2d3439343da51ba27b4bc24bcb44
- Size of remote file:
- 722 MB
- SHA256:
- 60cf5fd88f9d11bb9a81284c75ac855190b96083a6bfe712c26f092f7e12505d
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