Instructions to use lmh2011/w2v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmh2011/w2v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lmh2011/w2v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("lmh2011/w2v2") model = AutoModelForCTC.from_pretrained("lmh2011/w2v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lmh2011/w2v2: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/lmh2011/w2v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lmh2011/w2v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lmh2011/w2v2/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- ed50995bd7ba50f63f91cb4ba844aa99d71c717467e225cc245d5c090694eaf7
- Size of remote file:
- 378 MB
- SHA256:
- dfdba76fa55d53afa268df927888c127c3e0f164823f610cd24c0923694870a1
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