Instructions to use Sjdan/CA_1_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/CA_1_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/CA_1_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/CA_1_2") model = AutoModelForCTC.from_pretrained("Sjdan/CA_1_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/CA_1_2: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/CA_1_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/CA_1_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/CA_1_2/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 5c23963a9414b68a18019f557d60d487ad9fc9f479328dcbe3491d0063fc94de
- Size of remote file:
- 1.26 GB
- SHA256:
- fd89c092b066aad50b26c573dc5f8f1f0b6287dbf4891eb6af787fa82b69aa70
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