Instructions to use laura63/wav2vec2-base-finetuned-ks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use laura63/wav2vec2-base-finetuned-ks with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="laura63/wav2vec2-base-finetuned-ks")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("laura63/wav2vec2-base-finetuned-ks") model = AutoModelForAudioClassification.from_pretrained("laura63/wav2vec2-base-finetuned-ks", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from laura63/wav2vec2-base-finetuned-ks: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/laura63/wav2vec2-base-finetuned-ks/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://laura63/wav2vec2-base-finetuned-ks/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/laura63/wav2vec2-base-finetuned-ks/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 8d95cee00828f0371427797a985182ed8a51499db907997c88c85acfbb5f66e2
- Size of remote file:
- 378 MB
- SHA256:
- 52d4a582c3057fc9db9a14fe610f12ebccc5b2048f0c152192be4ecdcded3b5e
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