Instructions to use niclas/models_sv_eric_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use niclas/models_sv_eric_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="niclas/models_sv_eric_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("niclas/models_sv_eric_1") model = AutoModelForCTC.from_pretrained("niclas/models_sv_eric_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from niclas/models_sv_eric_1: direct link, hf CLI and curl.
- Browser
- Download file 2.8 kB
-
https://huggingface.co/niclas/models_sv_eric_1/resolve/main/training_args.bin
- Command line
-
hf download hf://niclas/models_sv_eric_1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/niclas/models_sv_eric_1/resolve/main/training_args.bin
2.8 kB
- Xet hash:
- 582591993ebc04070c2716a1f57f179f3c92c82c9e65fc2f267b381740c416e9
- Size of remote file:
- 2.8 kB
- SHA256:
- a5fa30ff12e39a8904c9087579c03235db4dfb3f1220cea38ab3ae49d46a4340
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