Instructions to use microsoft/wavlm-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/wavlm-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="microsoft/wavlm-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("microsoft/wavlm-base") model = AutoModel.from_pretrained("microsoft/wavlm-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
Verified signatureThis is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.637a84b
-
Update README.md
efa81aa
-
Update preprocessor_config.json
f7a1291
-
correct
14e28e3 -
Update config.json
d523a0b
-
Merge branch 'main' of https://huggingface.co/microsoft/wavlm-base into main
1c44dff -
add base model
9575613
-
Update README.md
449f1a0 -
Create README.md
eebdcd0
-
upload model
d359325 -
initial commit
c4bc3c2