Instructions to use vonewman/mind_audio_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vonewman/mind_audio_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="vonewman/mind_audio_classification_model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("vonewman/mind_audio_classification_model") model = AutoModelForAudioClassification.from_pretrained("vonewman/mind_audio_classification_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dc6b4fe2a27713336cae1560cbcb600adda35cbc61e584b1d02a6d985f9a361e
- Size of remote file:
- 378 MB
- SHA256:
- e1c5642bac135a968930cf81a4e8d5e52976c41b4626dae75fac171f51b04acd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.