Instructions to use cloudwalkerw/wavlm-base_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudwalkerw/wavlm-base_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="cloudwalkerw/wavlm-base_4")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("cloudwalkerw/wavlm-base_4") model = AutoModelForAudioClassification.from_pretrained("cloudwalkerw/wavlm-base_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cloudwalkerw/wavlm-base_4: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/cloudwalkerw/wavlm-base_4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cloudwalkerw/wavlm-base_4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cloudwalkerw/wavlm-base_4/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 30a9614ae48bbad3b59471f1a27cbd9f56fecec332ef964def9b7b30ce8bc3c7
- Size of remote file:
- 378 MB
- SHA256:
- 561fff97b1230ef647f3feec77ee73cecdc6e7223a6506edae3732d692415551
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