Instructions to use superb/wav2vec2-large-superb-ks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use superb/wav2vec2-large-superb-ks with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="superb/wav2vec2-large-superb-ks")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("superb/wav2vec2-large-superb-ks") model = AutoModelForAudioClassification.from_pretrained("superb/wav2vec2-large-superb-ks", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from superb/wav2vec2-large-superb-ks: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/superb/wav2vec2-large-superb-ks/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://superb/wav2vec2-large-superb-ks/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/superb/wav2vec2-large-superb-ks/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 9c3880ec33db234a4947841c8069686ac3e9e4a7dbc4537a2fb224cef88cf948
- Size of remote file:
- 1.26 GB
- SHA256:
- be36f743cf72df29a247523f697f35fab379511ea935547fda6fa79ca9c9e874
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