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 training_args.bin from ebonazza2910/model: direct link, hf CLI and curl.
- Browser
- Download file 2.86 kB
-
https://huggingface.co/ebonazza2910/model/resolve/main/training_args.bin
- Command line
-
hf download hf://ebonazza2910/model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ebonazza2910/model/resolve/main/training_args.bin
2.86 kB
- Xet hash:
- 4d906c31209b982be34eedbc560f7e000b2dc02ce043484cfcce372f81b6b16d
- Size of remote file:
- 2.86 kB
- SHA256:
- 10249b78a2b02e34cfd9941c5bf627b1da3c78cdd38127bb89a9cd3b3afe0ccc
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