Instructions to use Hemg/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Hemg/model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Hemg/model") model = AutoModelForAudioClassification.from_pretrained("Hemg/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c5aecdd271420cc00434d179f964e92fc666d98ae395a0b2603110457602911
- Size of remote file:
- 4.54 kB
- SHA256:
- 4236b8e5a01b11d035988457a0401bfe82e1d87bbe4ae42ecf51120f946e2d58
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