Feature Extraction
Transformers
Safetensors
English
usad
automatic-speech-recognition
audio-classification
audio
speech
music
custom_code
Instructions to use MIT-SLS/USAD-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MIT-SLS/USAD-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MIT-SLS/USAD-Large", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MIT-SLS/USAD-Large", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from MIT-SLS/USAD-Large: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/MIT-SLS/USAD-Large/resolve/main/model.safetensors
- Command line
-
hf download hf://MIT-SLS/USAD-Large/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/MIT-SLS/USAD-Large/resolve/main/model.safetensors
1.34 GB
- Xet hash:
- 0490f84a65d29e2ff90eb1b993b4987530c463ad3849e5f7010e3ec43ef7fffc
- Size of remote file:
- 1.34 GB
- SHA256:
- e5b8f98da245729082692545783647fdcd2164d0b144456249e9f8944e6e5fd6
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