Feature Extraction
Transformers
Safetensors
English
usad2
automatic-speech-recognition
audio-classification
audio
speech
music
custom_code
Instructions to use MIT-SLS/USAD2-XLarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MIT-SLS/USAD2-XLarge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MIT-SLS/USAD2-XLarge", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MIT-SLS/USAD2-XLarge", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57819269a7864d158d06b6641dc0d35f2f1ce7d86a189df97201b27b17459aff
- Size of remote file:
- 2.78 GB
- SHA256:
- 2763a3356aa2257cb2994ee74f3dcb92b55e4e44923836740018318c1585d1b6
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