Instructions to use hsali/wav2vec2_e_emo__d100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsali/wav2vec2_e_emo__d100 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("hsali/wav2vec2_e_emo__d100") model = Wav2Vec2ForSpeechClassification.from_pretrained("hsali/wav2vec2_e_emo__d100", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hsali/wav2vec2_e_emo__d100: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/hsali/wav2vec2_e_emo__d100/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hsali/wav2vec2_e_emo__d100/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hsali/wav2vec2_e_emo__d100/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- c0e834a5726adc92313e283d0b4c1d99ea81e832d6eb9d837dc7ca9f551d1618
- Size of remote file:
- 378 MB
- SHA256:
- 79dda17cb45224788da921cc3484b13f52f006d538fac07146340f0cf058337d
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