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 pytorch_model.bin from GENG/hubert_ls_2500: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/GENG/hubert_ls_2500/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://GENG/hubert_ls_2500@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/GENG/hubert_ls_2500/resolve/refs%2Fpr%2F1/pytorch_model.bin
378 MB
- Xet hash:
- 4ff9519ded0561ab9f8705887d04c8d4c5c756c02ce1bc05548b32eeb45565b9
- Size of remote file:
- 378 MB
- SHA256:
- 744bd6bba12fbab394bbdc7a5a45cfd878d2ac05d20818f5cfdcb7472e400110
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