Instructions to use lllFaNToMlll/wac2vec-lllfantomlll with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lllFaNToMlll/wac2vec-lllfantomlll with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lllFaNToMlll/wac2vec-lllfantomlll")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("lllFaNToMlll/wac2vec-lllfantomlll") model = AutoModelForCTC.from_pretrained("lllFaNToMlll/wac2vec-lllfantomlll", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lllFaNToMlll/wac2vec-lllfantomlll: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/lllFaNToMlll/wac2vec-lllfantomlll/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lllFaNToMlll/wac2vec-lllfantomlll/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lllFaNToMlll/wac2vec-lllfantomlll/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 8230b7a8cbfb8c6c5f738e97aafa20d7cb7d44b1d1e018cd6ca2c717c015563e
- Size of remote file:
- 378 MB
- SHA256:
- d557b80025e697ea1c461a37c6fd130be79d2b1e2587df796c8d9a6c059b072f
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