Instructions to use spudnik/wavlm-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spudnik/wavlm-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="spudnik/wavlm-emotion")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("spudnik/wavlm-emotion") model = AutoModelForAudioClassification.from_pretrained("spudnik/wavlm-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 782355bc1d7a61717cc89786c52912116133828e8c28921568bfc9a509966fcc
- Size of remote file:
- 378 MB
- SHA256:
- 99000f3e1a9057e29f9515df0bfc450d77194fb2ed4dc2ede8d6f025c6748637
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