Instructions to use MU-NLPC/whisper-small-audio-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MU-NLPC/whisper-small-audio-captioning with Transformers:
# Load model directly from transformers import AutoProcessor, WhisperForAudioCaptioning processor = AutoProcessor.from_pretrained("MU-NLPC/whisper-small-audio-captioning") model = WhisperForAudioCaptioning.from_pretrained("MU-NLPC/whisper-small-audio-captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e969a16737c7e04665629ea6a9092b88b3b3a9dc2d27d787419ca882c021292
- Size of remote file:
- 967 MB
- SHA256:
- 84b176d773e4b62be1190dc723430959838d954642fc00c5be406a32f9208b0d
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