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-Large-Plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MIT-SLS/USAD2-Large-Plus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MIT-SLS/USAD2-Large-Plus", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MIT-SLS/USAD2-Large-Plus", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 976c60ccde2a3a979a186f2ef910cc57a461bc8e1f1ef4d89e01d298c1f968ac
- Size of remote file:
- 1.34 GB
- SHA256:
- b32e188fde6cf3a2b060104917fedc0e3afd6309fe0fe7e3fe72652b71088aeb
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