Instructions to use niclas/model_sv_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use niclas/model_sv_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="niclas/model_sv_5")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("niclas/model_sv_5") model = AutoModelForCTC.from_pretrained("niclas/model_sv_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from niclas/model_sv_5: direct link, hf CLI and curl.
- Browser
- Download file 2.8 kB
-
https://huggingface.co/niclas/model_sv_5/resolve/main/training_args.bin
- Command line
-
hf download hf://niclas/model_sv_5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/niclas/model_sv_5/resolve/main/training_args.bin
2.8 kB
- Xet hash:
- 42a1f3c395efe7fe81354a4b1e2a898285d89db43f88a399f5281e47e4dbaf92
- Size of remote file:
- 2.8 kB
- SHA256:
- ca5498cd66d8456487586e53e11eed4f96d9874e18a6dab23d703780a0f5e2c9
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