Feature Extraction
Transformers
Safetensors
English
usad
automatic-speech-recognition
audio-classification
audio
speech
music
custom_code
Instructions to use MIT-SLS/USAD-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MIT-SLS/USAD-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MIT-SLS/USAD-Base", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MIT-SLS/USAD-Base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e2ea6f800078b003d4844377ac7c562b6e8951a037813fe13d3fb036f4db458
- Size of remote file:
- 389 MB
- SHA256:
- d7c3dd1a8ae999fc8152f52a09083369dd3d8cdfaa1af9bff01ebc39ffb9b314
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