Feature Extraction
Transformers
Safetensors
English
usad2
automatic-speech-recognition
audio-classification
audio
speech
music
custom_code
Instructions to use MIT-SLS/USAD2-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MIT-SLS/USAD2-Small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MIT-SLS/USAD2-Small", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MIT-SLS/USAD2-Small", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- faf26c51e71037852096373df2a80e46fe10b8530636e23c4da12d4e00d20ec5
- Size of remote file:
- 99.1 MB
- SHA256:
- 28a4e9c6b52ad27ccba372041c6e3565312bb6bf2d79ceb0ee943c96e78221d5
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