Feature Extraction
Transformers
Safetensors
English
usad
automatic-speech-recognition
audio-classification
audio
speech
music
custom_code
Instructions to use MIT-SLS/USAD-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MIT-SLS/USAD-Small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MIT-SLS/USAD-Small", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MIT-SLS/USAD-Small", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9dcf909fe76abc8750bac3e3f6ce76cc6a8aeeedf73ae802762be6e86fb0a17e
- Size of remote file:
- 99.1 MB
- SHA256:
- 7bbbdadf007983b65659e9a5f605888370e9fbde2c16498ebe5d48e2bf3db2b8
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