Instructions to use niclas/model_sv_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use niclas/model_sv_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="niclas/model_sv_4")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("niclas/model_sv_4") model = AutoModelForCTC.from_pretrained("niclas/model_sv_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from niclas/model_sv_4: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/niclas/model_sv_4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://niclas/model_sv_4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/niclas/model_sv_4/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 95fe5b69a491a07e2e8ce3efb1d25613e6263c0d80741e3d860f4cef4c77f8ce
- Size of remote file:
- 1.26 GB
- SHA256:
- 29b42968c4992f2646012ccae62d289fc3593c8a17f8c806e4fd9a3b57463eaa
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