Instructions to use Sjdan/cls_6ep1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/cls_6ep1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("Sjdan/cls_6ep1") model = Wav2Vec2ForSpeechClassification.from_pretrained("Sjdan/cls_6ep1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/cls_6ep1: direct link, hf CLI and curl.
- Browser
- Download file 1.27 GB
-
https://huggingface.co/Sjdan/cls_6ep1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/cls_6ep1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/cls_6ep1/resolve/main/pytorch_model.bin
1.27 GB
- Xet hash:
- c7d45caadb6624c50f1f716f7e2c26b802a69a52d4486a39c1bbb8c0b7c7c863
- Size of remote file:
- 1.27 GB
- SHA256:
- e27ed2450e4c503d35e1f2765a1dd51c785d76144c8eb96aae639bb7f7974a1b
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