Instructions to use facebook/wav2vec2-conformer-rel-pos-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/wav2vec2-conformer-rel-pos-large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") model = AutoModelForPreTraining.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/wav2vec2-conformer-rel-pos-large: direct link, hf CLI and curl.
- Browser
- Download file 2.48 GB
-
https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/wav2vec2-conformer-rel-pos-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large/resolve/main/pytorch_model.bin
2.48 GB
- Xet hash:
- ac4723e795155e291e80df57f79a7c0d839712b8a701cecd58dcc33b801662ef
- Size of remote file:
- 2.48 GB
- SHA256:
- 002baf81824168fb46b1027c7d03a40fc94ba20d719740168e01a2d181288c95
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