Instructions to use hsali/finetune_emo_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsali/finetune_emo_base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("hsali/finetune_emo_base") model = Wav2Vec2ForSpeechClassification.from_pretrained("hsali/finetune_emo_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hsali/finetune_emo_base: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/hsali/finetune_emo_base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hsali/finetune_emo_base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hsali/finetune_emo_base/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 4acc913dd011105bc5fe89145cfa46bee28a2458af1ca4d5c343aa034d368364
- Size of remote file:
- 378 MB
- SHA256:
- 39e9c0372abd014108ddcd62ecf10c14cc62145fe5620da70dc256ff148fa13b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.