Instructions to use cloudwalkerw/wavlm-base_2_predict with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudwalkerw/wavlm-base_2_predict with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="cloudwalkerw/wavlm-base_2_predict")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("cloudwalkerw/wavlm-base_2_predict") model = AutoModelForAudioClassification.from_pretrained("cloudwalkerw/wavlm-base_2_predict", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cloudwalkerw/wavlm-base_2_predict: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/cloudwalkerw/wavlm-base_2_predict/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cloudwalkerw/wavlm-base_2_predict/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cloudwalkerw/wavlm-base_2_predict/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 567576827b535f3f0ef00654025d62456f875fd754b208ce456afc0d86b2c648
- Size of remote file:
- 378 MB
- SHA256:
- 995961f9d5cb977c9ec869867645f4ac07038cef7e8b59e6859ef5501c21181e
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