Instructions to use facebook/wav2vec2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/wav2vec2-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base") model = AutoModelForPreTraining.from_pretrained("facebook/wav2vec2-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/wav2vec2-base: direct link, hf CLI and curl.
- Browser
- Download file 380 MB
-
https://huggingface.co/facebook/wav2vec2-base/resolve/refs%2Fpr%2F8/pytorch_model.bin
- Command line
-
hf download hf://facebook/wav2vec2-base@refs/pr/8/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/wav2vec2-base/resolve/refs%2Fpr%2F8/pytorch_model.bin
380 MB
- Xet hash:
- cbb392fbf9082ad779fee308f5447f008ce02330dc49e48ba00b0f4e01b3d242
- Size of remote file:
- 380 MB
- SHA256:
- 3249fe98bfc62fcbc26067f724716a6ec49d12c4728a2af1df659013905dff21
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