Instructions to use TisNam/emo_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TisNam/emo_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="TisNam/emo_model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("TisNam/emo_model") model = AutoModelForAudioClassification.from_pretrained("TisNam/emo_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d7b521d92a7a94e6f9734c278db0939725835bd38806b472aef3760e53e03cb
- Size of remote file:
- 4.92 kB
- SHA256:
- decb07fd61901eb7248ec185cc960ae2d2777a8530880439ac805c206e177d0d
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