Instructions to use niclas/model_sv_working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use niclas/model_sv_working with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="niclas/model_sv_working")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("niclas/model_sv_working") model = AutoModelForCTC.from_pretrained("niclas/model_sv_working", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from niclas/model_sv_working: direct link, hf CLI and curl.
- Browser
- Download file 2.8 kB
-
https://huggingface.co/niclas/model_sv_working/resolve/main/training_args.bin
- Command line
-
hf download hf://niclas/model_sv_working/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/niclas/model_sv_working/resolve/main/training_args.bin
2.8 kB
- Xet hash:
- 64457a75765b287de71f2c194050ddc937f65e74942201bef4262ee919b52bb7
- Size of remote file:
- 2.8 kB
- SHA256:
- 453aa0f35837dbc1a182dac9506c30b296ce84e33d2188b2221f6b9f47ade219
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