Instructions to use JovialValley/model_broadclass_onSet1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JovialValley/model_broadclass_onSet1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="JovialValley/model_broadclass_onSet1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("JovialValley/model_broadclass_onSet1") model = AutoModelForCTC.from_pretrained("JovialValley/model_broadclass_onSet1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from JovialValley/model_broadclass_onSet1: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/JovialValley/model_broadclass_onSet1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://JovialValley/model_broadclass_onSet1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/JovialValley/model_broadclass_onSet1/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- fe732ba026f2ed655c74d1c8fe1f4586359470f9167d4554f56878bd7255f1b4
- Size of remote file:
- 1.26 GB
- SHA256:
- 369b769a95b34dd1a03cd616704598b8102646bad2e077c09ff013d62426e98e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.