Instructions to use cloudwalkerw/wavlm-base_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudwalkerw/wavlm-base_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="cloudwalkerw/wavlm-base_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("cloudwalkerw/wavlm-base_2") model = AutoModelForAudioClassification.from_pretrained("cloudwalkerw/wavlm-base_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cloudwalkerw/wavlm-base_2: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/cloudwalkerw/wavlm-base_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cloudwalkerw/wavlm-base_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cloudwalkerw/wavlm-base_2/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 12394a9a36eced88a208ba59d6e2bfc51ce9ab1536ef5b8a93b299206cc7507b
- Size of remote file:
- 378 MB
- SHA256:
- 4afee26448c4b6ece687a1108698f2b7b9f1c98e1653472a01f597d6473e29ec
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