Instructions to use cloudwalkerw/wavlm-base_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudwalkerw/wavlm-base_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="cloudwalkerw/wavlm-base_5")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("cloudwalkerw/wavlm-base_5") model = AutoModelForAudioClassification.from_pretrained("cloudwalkerw/wavlm-base_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cloudwalkerw/wavlm-base_5: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/cloudwalkerw/wavlm-base_5/resolve/main/training_args.bin
- Command line
-
hf download hf://cloudwalkerw/wavlm-base_5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cloudwalkerw/wavlm-base_5/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 1b6b865e88021b49a50f4ad2a5d9d7ace1a5123d8fd04b10e15fd213aa2f897f
- Size of remote file:
- 4.09 kB
- SHA256:
- 38a1ef5b8dd1e669f7a36a7e149b0bd9bcc6922b8325393416497ef3012f4f4e
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