Instructions to use tomasolo/hubert_audio_classification_tomasolo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tomasolo/hubert_audio_classification_tomasolo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="tomasolo/hubert_audio_classification_tomasolo")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("tomasolo/hubert_audio_classification_tomasolo") model = AutoModelForAudioClassification.from_pretrained("tomasolo/hubert_audio_classification_tomasolo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tomasolo/hubert_audio_classification_tomasolo: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/tomasolo/hubert_audio_classification_tomasolo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tomasolo/hubert_audio_classification_tomasolo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tomasolo/hubert_audio_classification_tomasolo/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 15344ade1d9b7912a8f263b5a34c030c45ffce7b95aea6a6d6ab6fd5011993d6
- Size of remote file:
- 378 MB
- SHA256:
- 2e3e9ff0783fb1eaa64744ed95bffca9af106cb3b44819aa7729eb0a577067b4
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