Instructions to use Ammok/audio_classification_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ammok/audio_classification_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Ammok/audio_classification_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Ammok/audio_classification_1") model = AutoModelForAudioClassification.from_pretrained("Ammok/audio_classification_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ammok/audio_classification_1: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/Ammok/audio_classification_1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ammok/audio_classification_1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ammok/audio_classification_1/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- ee621f8db263798631d391128d53d33c3544139dcdf1c02dc508a65e7518da07
- Size of remote file:
- 378 MB
- SHA256:
- 7dc24576c08e3f82024a1485b7a7122e594dcb2fd9797b8f317509877491b121
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