Feature Extraction
Transformers
Safetensors
English
usad2
image-feature-extraction
automatic-speech-recognition
audio-classification
audio
speech
music
custom_code
Instructions to use MIT-SLS/USAD2-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MIT-SLS/USAD2-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MIT-SLS/USAD2-Base", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MIT-SLS/USAD2-Base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e730b958dacc4aa97c9b50f217c567c47401817a1533a6317e35cf490aef91a
- Size of remote file:
- 389 MB
- SHA256:
- e761017608064a04b6bd7d8a9aab3303bf1dd66c0f85c668e95d68a4d767b1d3
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