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 pytorch_model.bin from niclas/model_en_2: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/niclas/model_en_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://niclas/model_en_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/niclas/model_en_2/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 8c5ae2e4daffc4b0429b441efafa8043be825bdfae874fc87aa6017afdc45840
- Size of remote file:
- 1.26 GB
- SHA256:
- b9fcc4c5e2ba2aef7a9c6d5d57db1acca2bf6600c8faa41f2352288400afcc87
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