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:
- 24229602092438bc332839945a00474894ee6f96290052032c2c39462648b519
- Size of remote file:
- 378 MB
- SHA256:
- 55af4e2c84df224eb60cd97db967019119d40aa37e4ba9325ac2abd56a6bcfd4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.