Instructions to use kensvin/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kensvin/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="kensvin/audio_classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("kensvin/audio_classification") model = AutoModelForAudioClassification.from_pretrained("kensvin/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ecd310ca31ace68162a359d8f9ed6fd3b0c4c5a9708b31dbabb3780c81bc9d8f
- Size of remote file:
- 4.09 kB
- SHA256:
- d45d169fd1bcbe2021c047b1cff3b813b4da7b5a34fccf17ac942fb1f47877cf
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