Instructions to use Sjdan/mst_hp3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/mst_hp3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/mst_hp3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/mst_hp3") model = AutoModelForCTC.from_pretrained("Sjdan/mst_hp3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/mst_hp3: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/mst_hp3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/mst_hp3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/mst_hp3/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 0b8552392c5456eea34bd0725caa7d1b002afce5d695c286d3f9bcd2bcc8a6e5
- Size of remote file:
- 1.26 GB
- SHA256:
- 16fe6e83aaa5d1e22536dde6a39e7188fdd7e6293cbc8ffc384a1c55330d6449
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