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 training_args.bin from cloudwalkerw/wavlm-base_2: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/cloudwalkerw/wavlm-base_2/resolve/main/training_args.bin
- Command line
-
hf download hf://cloudwalkerw/wavlm-base_2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cloudwalkerw/wavlm-base_2/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 826157f44a60ec9a5058930ae42f342270883b199a767ff6609e538d50e47c45
- Size of remote file:
- 4.09 kB
- SHA256:
- 181560a652f639b92d4e9e36db5aa150f186d0d10630e42ead9328f89bcd7c4c
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