Instructions to use bezzam/xcodec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bezzam/xcodec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bezzam/xcodec")# Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("bezzam/xcodec") model = AutoModel.from_pretrained("bezzam/xcodec") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 214e3b6789418abe098a65b5742a0137038fe520c93a9f4bc7f03f7e41704adb
- Size of remote file:
- 710 MB
- SHA256:
- 6191971294c216bf6fb7f38fb39e69267a2e3eea6b5469a0952806923b901ce5
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