Instructions to use Drigoro/hubert_audio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Drigoro/hubert_audio with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Drigoro/hubert_audio")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Drigoro/hubert_audio") model = AutoModelForAudioClassification.from_pretrained("Drigoro/hubert_audio", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Drigoro/hubert_audio: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/Drigoro/hubert_audio/resolve/main/training_args.bin
- Command line
-
hf download hf://Drigoro/hubert_audio/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Drigoro/hubert_audio/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 614342612aa08b78722c0be196aeffa8126b0958704b145c454597018b88d1ba
- Size of remote file:
- 5.11 kB
- SHA256:
- 0e35d523518c47f6d4d27a6ebc7bb12d84dc236beeeb43ce0d208a77ecb2b6c6
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