Instructions to use Sjdan/C3_A_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/C3_A_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/C3_A_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/C3_A_2") model = AutoModelForCTC.from_pretrained("Sjdan/C3_A_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/C3_A_2: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/C3_A_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/C3_A_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/C3_A_2/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- a0b5ac0d02826232fd70aa7054479903ab5bced04f58403c3ad7a57920f06cb4
- Size of remote file:
- 1.26 GB
- SHA256:
- 685d5d52f376d5a6f4e126d59a213d85975da07c0db62a4f8c6966e51a4c5600
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.