Instructions to use niclas/models_sv_7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use niclas/models_sv_7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="niclas/models_sv_7")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("niclas/models_sv_7") model = AutoModelForCTC.from_pretrained("niclas/models_sv_7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from niclas/models_sv_7: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/niclas/models_sv_7/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://niclas/models_sv_7/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/niclas/models_sv_7/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- f5593698dec70a73cea2706974c5f87433f1a9cde5859d9cebc40b8f3010412c
- Size of remote file:
- 1.26 GB
- SHA256:
- e75ac220f32b4e261d51239d9452ee47a9074ed350ed57a47e6181b604be5ff3
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