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 pytorch_model.bin from niclas/models_sv_eric_1: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/niclas/models_sv_eric_1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://niclas/models_sv_eric_1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/niclas/models_sv_eric_1/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 0264c02a3afb86c3a2f9cbf4e9b5300ef481a44268124206e91e8feaed28fbdd
- Size of remote file:
- 1.26 GB
- SHA256:
- a290fe2b54e5c008c5f7995e6fccdf763ea3336bc0cbe58869f22614af4e6227
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