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-XLarge-Plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MIT-SLS/USAD2-XLarge-Plus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MIT-SLS/USAD2-XLarge-Plus", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MIT-SLS/USAD2-XLarge-Plus", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4809f5bab47acb91b8a7bff88e8372cb0e59303c4f4c8b8290ad5f1440c3be9
- Size of remote file:
- 2.78 GB
- SHA256:
- 61772195c385a049b07ee7b34ba91bb9a77f1e936c1583a303f9d17c26445097
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