Instructions to use cloudwalkerw/wavlm-base_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudwalkerw/wavlm-base_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="cloudwalkerw/wavlm-base_3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("cloudwalkerw/wavlm-base_3") model = AutoModelForAudioClassification.from_pretrained("cloudwalkerw/wavlm-base_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cloudwalkerw/wavlm-base_3: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/cloudwalkerw/wavlm-base_3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cloudwalkerw/wavlm-base_3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cloudwalkerw/wavlm-base_3/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 01a7cb53c2a0b65523f06703c684e2dcc5f24d59e7f84fb5809454117e8310ba
- Size of remote file:
- 378 MB
- SHA256:
- 923826c892e8069448904946ade5daf9ac9a68ec627472bc76a32cc9e3c671e2
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