Instructions to use niclas/model_en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use niclas/model_en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="niclas/model_en")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("niclas/model_en") model = AutoModelForCTC.from_pretrained("niclas/model_en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from niclas/model_en: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/niclas/model_en/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://niclas/model_en/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/niclas/model_en/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 0493b5d72dafcd1346de12440c9321f5e438877565f483d8f50b0f665254a385
- Size of remote file:
- 1.26 GB
- SHA256:
- b38a27a253e3ba934cc4b048e0585ee84f07cd74c78c22f5cee8a498520d29ad
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