Instructions to use werent4/emo-gliclass-audio-bi-1-wds-0.015-red-sum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use werent4/emo-gliclass-audio-bi-1-wds-0.015-red-sum with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("werent4/emo-gliclass-audio-bi-1-wds-0.015-red-sum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from werent4/emo-gliclass-audio-bi-1-wds-0.015-red-sum: direct link, hf CLI and curl.
- Browser
- Download file 988 MB
-
https://huggingface.co/werent4/emo-gliclass-audio-bi-1-wds-0.015-red-sum/resolve/main/model.safetensors
- Command line
-
hf download hf://werent4/emo-gliclass-audio-bi-1-wds-0.015-red-sum/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/werent4/emo-gliclass-audio-bi-1-wds-0.015-red-sum/resolve/main/model.safetensors
988 MB
- Xet hash:
- f57ba99f54165d95d25d0f1f5ae12472526d9d4174c6373cdc45eccfc224a0c0
- Size of remote file:
- 988 MB
- SHA256:
- aa6f54c414be691b92a86994c7dcf4845872537e6dff951eb9f07c974c36c5ee
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