Audio Classification
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use JasHugF/whisper-tiny-zero-shot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JasHugF/whisper-tiny-zero-shot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="JasHugF/whisper-tiny-zero-shot")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("JasHugF/whisper-tiny-zero-shot") model = AutoModelForAudioClassification.from_pretrained("JasHugF/whisper-tiny-zero-shot") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c0f29eeb6c1afb0607b53b389428e03bfe7cf3eade285b834364723fb229b2e1
- Size of remote file:
- 33.2 MB
- SHA256:
- 94a215bdc644a8cf18fa1e6a6c4ba501fa77bfb0eca895caa7f7a3e0d70539df
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