Audio Classification
Transformers
PyTorch
hubert
feature-extraction
music
audio
speech
audio-representation-learning
arch-benchmark
general-audio
Instructions to use ALM/hubert-base-audioset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ALM/hubert-base-audioset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="ALM/hubert-base-audioset")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("ALM/hubert-base-audioset") model = AutoModel.from_pretrained("ALM/hubert-base-audioset") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 591a1d672beab54a4d7496a50e1f32486e62aa9cca83a6497c442bf8eca6350c
- Size of remote file:
- 378 MB
- SHA256:
- 5c08dc8ba5aea58b2217961a8c0d17af690667e8829d7192c08f92238cc5b85a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.