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 training_args.bin from laura63/wav2vec2-base-finetuned-ks: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/laura63/wav2vec2-base-finetuned-ks/resolve/main/training_args.bin
- Command line
-
hf download hf://laura63/wav2vec2-base-finetuned-ks/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/laura63/wav2vec2-base-finetuned-ks/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- 80d953d8b25394fec18c828cfcf1cae5d67ee382461aea26d1fd2c0169ac4ebc
- Size of remote file:
- 3.96 kB
- SHA256:
- 84ffbe71174300cabf1f429c03f6be0dd5208fa23cf68a41242de47cc5d4eddc
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