Instructions to use pratt3000/wav2vec2-base-finetuned-ks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pratt3000/wav2vec2-base-finetuned-ks with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="pratt3000/wav2vec2-base-finetuned-ks")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("pratt3000/wav2vec2-base-finetuned-ks") model = AutoModelForAudioClassification.from_pretrained("pratt3000/wav2vec2-base-finetuned-ks", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from pratt3000/wav2vec2-base-finetuned-ks: direct link, hf CLI and curl.
- Browser
- Download file 2.93 kB
-
https://huggingface.co/pratt3000/wav2vec2-base-finetuned-ks/resolve/main/training_args.bin
- Command line
-
hf download hf://pratt3000/wav2vec2-base-finetuned-ks/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pratt3000/wav2vec2-base-finetuned-ks/resolve/main/training_args.bin
2.93 kB
- Xet hash:
- c4b1f940fcfa0743c7823de67e96b7508c63a0b04371849f137bf82f699352b6
- Size of remote file:
- 2.93 kB
- SHA256:
- 30da702794639a397b235ed9f00d705ef650553f4167ab87e72a40c001af542e
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