Instructions to use niclas/ATC_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use niclas/ATC_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="niclas/ATC_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("niclas/ATC_1") model = AutoModelForCTC.from_pretrained("niclas/ATC_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from niclas/ATC_1: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/niclas/ATC_1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://niclas/ATC_1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/niclas/ATC_1/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 392d519a9c099394b7819e096b724d438dcfbbf2ac194e34ccbaab3433eb0d3b
- Size of remote file:
- 1.26 GB
- SHA256:
- 6a81264da656b035b9c58bef9b9768a86791435e46ab953b89e9ec529514e9c1
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