Instructions to use wbcmthh42/wavlm_handcrafted_feat3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wbcmthh42/wavlm_handcrafted_feat3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="wbcmthh42/wavlm_handcrafted_feat3")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("wbcmthh42/wavlm_handcrafted_feat3") model = AutoModelForAudioClassification.from_pretrained("wbcmthh42/wavlm_handcrafted_feat3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f425b8a67d8fc4bad31512d3a3601eb78453799f6623047ad45457077c10d27a
- Size of remote file:
- 378 MB
- SHA256:
- c6ae08a499eef9002715b13e1ff3ab05e7b74d7a5d42665b45e8ecf8f8ebe5c1
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