Instructions to use ebonazza2910/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ebonazza2910/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ebonazza2910/model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("ebonazza2910/model") model = AutoModelForCTC.from_pretrained("ebonazza2910/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ebonazza2910/model: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/ebonazza2910/model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ebonazza2910/model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ebonazza2910/model/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- e2fc11176ca99186a715f69b3b263716fbc17998466c9dae2b5acb301dc06606
- Size of remote file:
- 1.26 GB
- SHA256:
- 4ec536f8e4c2b1474799fa347128ddf29061335af2d21b812a4e5156de75ceb1
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