Instructions to use cloudwalkerw/wavlm-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudwalkerw/wavlm-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="cloudwalkerw/wavlm-base")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("cloudwalkerw/wavlm-base") model = AutoModelForAudioClassification.from_pretrained("cloudwalkerw/wavlm-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 938cc830e45e2ae581d57e6b33b684e04cffaab5f997f1df8a649c3d58ff789d
- Size of remote file:
- 378 MB
- SHA256:
- 178b880ca8704331ad80cf0013760de30727c62b93c0c3b907dc4e2d8c797877
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