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 training_args.bin from cloudwalkerw/wavlm-base_4: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/cloudwalkerw/wavlm-base_4/resolve/main/training_args.bin
- Command line
-
hf download hf://cloudwalkerw/wavlm-base_4/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cloudwalkerw/wavlm-base_4/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- e0f4e83e96cf9a39ef9c178e883609fd68a5b747c251aea9b609b14d1ca251d2
- Size of remote file:
- 4.09 kB
- SHA256:
- 5ede3ecfc216941469cdddf25b8ae2edbd965e19ab158dac219ecc325f70b2f0
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