Instructions to use explorer7/wav2vec2-ks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use explorer7/wav2vec2-ks with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="explorer7/wav2vec2-ks")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("explorer7/wav2vec2-ks") model = AutoModelForAudioClassification.from_pretrained("explorer7/wav2vec2-ks", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from explorer7/wav2vec2-ks: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/explorer7/wav2vec2-ks/resolve/main/training_args.bin
- Command line
-
hf download hf://explorer7/wav2vec2-ks/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/explorer7/wav2vec2-ks/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- 208913688479f769e6bdb2c701b840f8671ae052e9a1978d26e63dfb9ae3858b
- Size of remote file:
- 5.78 kB
- SHA256:
- c45712f5f7a3a9080a89a9d8f9dd4a5da93ec8c60344eb0ecc87601e1e052fca
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