Instructions to use Ammar2k/trained_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ammar2k/trained_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Ammar2k/trained_model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Ammar2k/trained_model") model = AutoModelForAudioClassification.from_pretrained("Ammar2k/trained_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15cd57da2fd7c8052d2311ec5eea3c90088cac1e1c0c53a3a2371ba69dcc20df
- Size of remote file:
- 4.73 kB
- SHA256:
- aec00ae24edd5a2df8848aed86239d0ce57b5ef38d2c1ab713eb431d05d20bb3
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