Instructions to use krishnareddy/audio_classification_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krishnareddy/audio_classification_example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="krishnareddy/audio_classification_example")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("krishnareddy/audio_classification_example") model = AutoModelForAudioClassification.from_pretrained("krishnareddy/audio_classification_example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from krishnareddy/audio_classification_example: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/krishnareddy/audio_classification_example/resolve/main/model.safetensors
- Command line
-
hf download hf://krishnareddy/audio_classification_example/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/krishnareddy/audio_classification_example/resolve/main/model.safetensors
378 MB
- Xet hash:
- 069a1d23f15c8bab980ee83932d6c005c2d5b05646826e719d267b31c4bf3252
- Size of remote file:
- 378 MB
- SHA256:
- a55667f087c0e9db17de5f1cc60770e0a1dfef8427c7294a8fd1bf565fc22ba5
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