Instructions to use JovialValley/model_broadclass_onSet0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JovialValley/model_broadclass_onSet0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="JovialValley/model_broadclass_onSet0.1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("JovialValley/model_broadclass_onSet0.1") model = AutoModelForCTC.from_pretrained("JovialValley/model_broadclass_onSet0.1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from JovialValley/model_broadclass_onSet0.1: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/JovialValley/model_broadclass_onSet0.1/resolve/main/training_args.bin
- Command line
-
hf download hf://JovialValley/model_broadclass_onSet0.1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/JovialValley/model_broadclass_onSet0.1/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- 3f95947dff2e4ea9136f00ccddcffe638912d57570c79b576bbeda719b4d8055
- Size of remote file:
- 3.45 kB
- SHA256:
- b53c9f925ede21020b934e4022665012d8d372554e88c34fdaa4344e6238aeb7
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