Instructions to use Sjdan/mst_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/mst_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/mst_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/mst_1") model = AutoModelForCTC.from_pretrained("Sjdan/mst_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/mst_1: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/mst_1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/mst_1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/mst_1/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 09a39bd1ca9282641519141250f029904441b1094b63bd884534ff978376999d
- Size of remote file:
- 1.26 GB
- SHA256:
- 3504540f6e0574fcfe5495884fcfea529339ba47baec3c746a1ccd0849a923d2
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