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 training_args.bin from lllFaNToMlll/wac2vec-lllfantomlll: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/lllFaNToMlll/wac2vec-lllfantomlll/resolve/main/training_args.bin
- Command line
-
hf download hf://lllFaNToMlll/wac2vec-lllfantomlll/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/lllFaNToMlll/wac2vec-lllfantomlll/resolve/main/training_args.bin
2.99 kB
- Xet hash:
- 0fdb7d6fbb50cca49b845786ef1e35f3f7aa36e51c439b4a6d57d5f129ae0b2f
- Size of remote file:
- 2.99 kB
- SHA256:
- 4c7e893fffa275974e65337c0c14c07d3d82fe859d71be421735f7da14d49daf
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