Instructions to use NhatPham/wav2vec2-base-finetuned-ks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NhatPham/wav2vec2-base-finetuned-ks with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="NhatPham/wav2vec2-base-finetuned-ks")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("NhatPham/wav2vec2-base-finetuned-ks") model = AutoModelForAudioClassification.from_pretrained("NhatPham/wav2vec2-base-finetuned-ks") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3bd4c78f12a801aba1a2465547dfc9a790e423ae504d0a6cd0650f700eabac08
- Size of remote file:
- 378 MB
- SHA256:
- 492c9036700f6611b6320c48ae040be2259703291980940d23053b93593077a4
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