Instructions to use MU-NLPC/whisper-tiny-audio-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MU-NLPC/whisper-tiny-audio-captioning with Transformers:
# Load model directly from transformers import AutoProcessor, WhisperForAudioCaptioning processor = AutoProcessor.from_pretrained("MU-NLPC/whisper-tiny-audio-captioning") model = WhisperForAudioCaptioning.from_pretrained("MU-NLPC/whisper-tiny-audio-captioning") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3581a682e0c7070b1a041d8e761b3b9933e0bff2a56489cfcee01c0a7eab9c36
- Size of remote file:
- 151 MB
- SHA256:
- f8c77ef91b396939fab93d1e4b78a7307ccfadabc9982adb487fb1e564088be6
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