Instructions to use JovialValley/model_broadclass_onSet0try1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JovialValley/model_broadclass_onSet0try1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="JovialValley/model_broadclass_onSet0try1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("JovialValley/model_broadclass_onSet0try1") model = AutoModelForCTC.from_pretrained("JovialValley/model_broadclass_onSet0try1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from JovialValley/model_broadclass_onSet0try1: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/JovialValley/model_broadclass_onSet0try1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://JovialValley/model_broadclass_onSet0try1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/JovialValley/model_broadclass_onSet0try1/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 32a486f4d2fbc585e5e62b8a877822fb0a91735cdde8c473a3376eb20e85ef2d
- Size of remote file:
- 1.26 GB
- SHA256:
- 836065fa2821e8f01a1bd2e2f2c74ddb15c87e55798e43130531eaabc8688010
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