Instructions to use Sjdan/CA_SID_M14_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/CA_SID_M14_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/CA_SID_M14_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/CA_SID_M14_2") model = AutoModelForCTC.from_pretrained("Sjdan/CA_SID_M14_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/CA_SID_M14_2: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/CA_SID_M14_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/CA_SID_M14_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/CA_SID_M14_2/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 902f3b7f64a1409fb803cc0ea284eab89f20a5090e41353274439615bab6e367
- Size of remote file:
- 1.26 GB
- SHA256:
- 0376ebfd2443168832422edb09d11a1bb97fd43c5d719c00287cb24b401c2ae3
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