Instructions to use anton-l/wav2vec2-base-superb-sv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anton-l/wav2vec2-base-superb-sv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="anton-l/wav2vec2-base-superb-sv")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioXVector processor = AutoProcessor.from_pretrained("anton-l/wav2vec2-base-superb-sv") model = AutoModelForAudioXVector.from_pretrained("anton-l/wav2vec2-base-superb-sv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from anton-l/wav2vec2-base-superb-sv: direct link, hf CLI and curl.
- Browser
- Download file 404 MB
-
https://huggingface.co/anton-l/wav2vec2-base-superb-sv/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://anton-l/wav2vec2-base-superb-sv/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anton-l/wav2vec2-base-superb-sv/resolve/main/pytorch_model.bin
404 MB
- Xet hash:
- 092c831aecd73e5b2f7f7bf2f15189955e330fdb067cc234d59d96148f078f3e
- Size of remote file:
- 404 MB
- SHA256:
- dca2df146a2340fa99af66baee448b42560e73f2e4f10dc507eb5982987e0f7a
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