Instructions to use MU-NLPC/whisper-large-v2-audio-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MU-NLPC/whisper-large-v2-audio-captioning with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, WhisperForAudioCaptioning processor = AutoProcessor.from_pretrained("MU-NLPC/whisper-large-v2-audio-captioning") model = WhisperForAudioCaptioning.from_pretrained("MU-NLPC/whisper-large-v2-audio-captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MU-NLPC/whisper-large-v2-audio-captioning: direct link, hf CLI and curl.
- Browser
- Download file 6.17 GB
-
https://huggingface.co/MU-NLPC/whisper-large-v2-audio-captioning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MU-NLPC/whisper-large-v2-audio-captioning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MU-NLPC/whisper-large-v2-audio-captioning/resolve/main/pytorch_model.bin
6.17 GB
- Xet hash:
- 4c4c547e8e5f129f3b3b4a99c77079d08266159e03305930c30d84f1023fe12e
- Size of remote file:
- 6.17 GB
- SHA256:
- 1e6adc1993653de9983f5c703fb297a785097975c02daba7ee62a908c3f0324b
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