Instructions to use hf-audio/xcodec-hubert-librispeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-audio/xcodec-hubert-librispeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-audio/xcodec-hubert-librispeech")# pip install -U transformers accelerate # Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("hf-audio/xcodec-hubert-librispeech") model = AutoModel.from_pretrained("hf-audio/xcodec-hubert-librispeech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-audio/xcodec-hubert-librispeech: direct link, hf CLI and curl.
- Browser
- Download file 710 MB
-
https://huggingface.co/hf-audio/xcodec-hubert-librispeech/resolve/main/model.safetensors
- Command line
-
hf download hf://hf-audio/xcodec-hubert-librispeech/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-audio/xcodec-hubert-librispeech/resolve/main/model.safetensors
710 MB
- Xet hash:
- 20ea69537be71cc0d70ff2163b79a818c9829349b4d674bad662e85ea1e378dd
- Size of remote file:
- 710 MB
- SHA256:
- 6191971294c216bf6fb7f38fb39e69267a2e3eea6b5469a0952806923b901ce5
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