Instructions to use abletobetable/ast-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abletobetable/ast-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="abletobetable/ast-emotion")# Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("abletobetable/ast-emotion") model = AutoModelForAudioClassification.from_pretrained("abletobetable/ast-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dac3b84d43e214560261de15cdc7a66833434adf207e0da91fd1cf7d4b0dbb76
- Size of remote file:
- 3.58 kB
- SHA256:
- e62f6949d8a465c40af26ce46c389d06c180ffbba3050bd2ca4b07eedbc7e18f
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