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 training_args.bin from hsali/wav2vec2_e_emo__d100: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/hsali/wav2vec2_e_emo__d100/resolve/main/training_args.bin
- Command line
-
hf download hf://hsali/wav2vec2_e_emo__d100/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hsali/wav2vec2_e_emo__d100/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- c4b0b108147a1b0fc24fc616717aa67b5f2bff14b6ca3cf9aad4c3c49f154f28
- Size of remote file:
- 3.52 kB
- SHA256:
- 8c9f76d916bc7c278e59cf0744c35540361a8c1ad490f20b625407c6438f2697
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