Instructions to use JovialValley/model_broadclass_onSet2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JovialValley/model_broadclass_onSet2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="JovialValley/model_broadclass_onSet2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("JovialValley/model_broadclass_onSet2") model = AutoModelForCTC.from_pretrained("JovialValley/model_broadclass_onSet2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from JovialValley/model_broadclass_onSet2: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/JovialValley/model_broadclass_onSet2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://JovialValley/model_broadclass_onSet2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/JovialValley/model_broadclass_onSet2/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 8a6b2a23930971bbc59e6b3a24e6cd38cf9ab0072663b3f0fb4e4fb606592497
- Size of remote file:
- 1.26 GB
- SHA256:
- f817c37d3eb364fc0057ceb433c69b8d73ca3a002d6e0c9599d6ec31152f7323
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