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 training_args.bin from cloudwalkerw/wavlm-base_2_predict: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/cloudwalkerw/wavlm-base_2_predict/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://cloudwalkerw/wavlm-base_2_predict@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cloudwalkerw/wavlm-base_2_predict/resolve/refs%2Fpr%2F1/training_args.bin
4.09 kB
- Xet hash:
- 22f5ef7ee26cc296b5c76e42e367c23d89ce42ef49493412341d8382268b5eb5
- Size of remote file:
- 4.09 kB
- SHA256:
- b234f43693da9ffa8cc78e474bb9b22bba03d7fb6ccd0cc7dc9a736d4b8b5be6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.