Instructions to use eswardivi/mal_audio_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eswardivi/mal_audio_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="eswardivi/mal_audio_model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("eswardivi/mal_audio_model") model = AutoModelForAudioClassification.from_pretrained("eswardivi/mal_audio_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee782e6e23cdacd9e4ad74ecfe2d90c93d377a9f8b547036d79b129ef6dfb732
- Size of remote file:
- 4.54 kB
- SHA256:
- 6b1145e62a67c22c42e8ccfde8c60db164f918954a9e59a1f7f460e16ece4814
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