Instructions to use cloudwalkerw/wavlm-base_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudwalkerw/wavlm-base_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="cloudwalkerw/wavlm-base_5")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("cloudwalkerw/wavlm-base_5") model = AutoModelForAudioClassification.from_pretrained("cloudwalkerw/wavlm-base_5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23a13c625299af1bac4b48fbdcb5e1152bf00f27bd13a9692faa5ecbd359db3f
- Size of remote file:
- 378 MB
- SHA256:
- de0a99b3854d5f58ec1d6dec58c51ebf32bfaa14017da7497d38ef32385d284a
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