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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download __init__.py from MIT-SLS/USAD2-Small: direct link, hf CLI and curl.
- Browser
- Download file 0 Bytes
-
https://huggingface.co/MIT-SLS/USAD2-Small/resolve/main/__init__.py
- Command line
-
hf download hf://MIT-SLS/USAD2-Small/__init__.py
-
curl -L -o __init__.py https://huggingface.co/MIT-SLS/USAD2-Small/resolve/main/__init__.py
0 Bytes