Instructions to use salixc/dielect_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use salixc/dielect_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="salixc/dielect_classification_model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("salixc/dielect_classification_model") model = AutoModelForAudioClassification.from_pretrained("salixc/dielect_classification_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94e1094be412b5a6e3cfeb81eecd02f42b82d350d98b402b0e846b3d774dfb6d
- Size of remote file:
- 378 MB
- SHA256:
- c48512b3f46bfd51d4bbfa2b371280ec121cbf5d66ed1cb8491b37c533e05495
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