Instructions to use facebook/wav2vec2-large-robust with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/wav2vec2-large-robust with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("facebook/wav2vec2-large-robust") model = AutoModelForPreTraining.from_pretrained("facebook/wav2vec2-large-robust", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/wav2vec2-large-robust: direct link, hf CLI and curl.
- Browser
- Download file 1.27 GB
-
https://huggingface.co/facebook/wav2vec2-large-robust/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/wav2vec2-large-robust/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/wav2vec2-large-robust/resolve/main/pytorch_model.bin
1.27 GB
- Xet hash:
- fefaebfea67d2110ec3b67c3a9899494d7ce6a2f932fc857a01e8e3285c07150
- Size of remote file:
- 1.27 GB
- SHA256:
- 717f79822e6be28ed996955b74dff39f3ca131ba293212152119dce6aa7f3642
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