Instructions to use facebook/data2vec-audio-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-audio-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook/data2vec-audio-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-audio-base") model = AutoModel.from_pretrained("facebook/data2vec-audio-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/data2vec-audio-base: direct link, hf CLI and curl.
- Browser
- Download file 373 MB
-
https://huggingface.co/facebook/data2vec-audio-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/data2vec-audio-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/data2vec-audio-base/resolve/main/pytorch_model.bin
373 MB
- Xet hash:
- 28bbded89826704545eccdb1652f63ad23a1ed7b4c8e4642cf8f0529c5225736
- Size of remote file:
- 373 MB
- SHA256:
- 4fd2317db95a7d67d3e328647a06f3f90c1b30e2044024a56ccec18827841225
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