Feature Extraction
Transformers
Safetensors
English
usad2
automatic-speech-recognition
audio-classification
audio
speech
music
custom_code
Instructions to use MIT-SLS/USAD2-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MIT-SLS/USAD2-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MIT-SLS/USAD2-Large", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MIT-SLS/USAD2-Large", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e932d7ef722f99e16fd01498954e5f405a7211c3e5612a0d6d3cd8aba871fec
- Size of remote file:
- 1.34 GB
- SHA256:
- f8daf43902379e53b4ad67942d0854710a9317d3346c813f13707fe704d14d75
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