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 scheduler.pt from GENG/hubert_ls_2500: direct link, hf CLI and curl.
- Browser
- Download file 623 Bytes
-
https://huggingface.co/GENG/hubert_ls_2500/resolve/main/scheduler.pt
- Command line
-
hf download hf://GENG/hubert_ls_2500/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/GENG/hubert_ls_2500/resolve/main/scheduler.pt
623 Bytes
- Xet hash:
- a358b2ecb484adb2fdb1a6ae3851ef1c8bda4753ad8acc311975ad06c86c7316
- Size of remote file:
- 623 Bytes
- SHA256:
- 44312464efeb9319a55fd4e3752172215f3e8c527e11e35ec418cb9c7a180de9
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