Instructions to use jaeihn/kws_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaeihn/kws_embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="jaeihn/kws_embedding")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("jaeihn/kws_embedding") model = AutoModelForAudioClassification.from_pretrained("jaeihn/kws_embedding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jaeihn/kws_embedding: direct link, hf CLI and curl.
- Browser
- Download file 27 MB
-
https://huggingface.co/jaeihn/kws_embedding/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jaeihn/kws_embedding/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jaeihn/kws_embedding/resolve/main/pytorch_model.bin
27 MB
- Xet hash:
- f2a8c0b408199d91826291520bd94594887c4b7ba3cf16b1a5becdc5db418b50
- Size of remote file:
- 27 MB
- SHA256:
- c619e6b1e2cc3450c5d6b8e54263e1c8f019f1da5f9a048693f71c4298c23def
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