Instructions to use moonseok/result_Ver3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moonseok/result_Ver3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="moonseok/result_Ver3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("moonseok/result_Ver3") model = AutoModelForCTC.from_pretrained("moonseok/result_Ver3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from moonseok/result_Ver3: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/moonseok/result_Ver3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://moonseok/result_Ver3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/moonseok/result_Ver3/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- c3d25fda4ac398a0e51662205f98c8e9635c089dfeab4975a65dbb32c4f925bc
- Size of remote file:
- 378 MB
- SHA256:
- 874718239636af8c18a672af8fe7bc4e19a7fbfa217d81b89ae9f1dc45212bb5
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