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 trainer_state.json from GENG/hubert_ls_2500: direct link, hf CLI and curl.
- Browser
- Download file 1.44 kB
-
https://huggingface.co/GENG/hubert_ls_2500/resolve/main/trainer_state.json
- Command line
-
hf download hf://GENG/hubert_ls_2500/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/GENG/hubert_ls_2500/resolve/main/trainer_state.json
1.44 kB
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| "best_model_checkpoint": null, | |
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| "global_step": 1500, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
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| "learning_rate": 4.9800000000000004e-05, | |
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| "step": 500 | |
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| "step": 1000 | |
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| "trial_name": null, | |
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| } | |