Instructions to use niclas/model_en_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use niclas/model_en_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="niclas/model_en_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("niclas/model_en_2") model = AutoModelForCTC.from_pretrained("niclas/model_en_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from niclas/model_en_2: direct link, hf CLI and curl.
- Browser
- Download file 2.8 kB
-
https://huggingface.co/niclas/model_en_2/resolve/main/training_args.bin
- Command line
-
hf download hf://niclas/model_en_2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/niclas/model_en_2/resolve/main/training_args.bin
2.8 kB
- Xet hash:
- bc20270273dcf421171b20efd6ca026f71ac9b2e9aa2fc1d39ea7cf1bd86df20
- Size of remote file:
- 2.8 kB
- SHA256:
- 92ffa817ebe8d1980892e94f9eea5a413a19b2e4d06102b9090af0e88685ba68
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