Instructions to use ntoldalagi/C0_LID_DEV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ntoldalagi/C0_LID_DEV with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ntoldalagi/C0_LID_DEV")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("ntoldalagi/C0_LID_DEV") model = AutoModelForCTC.from_pretrained("ntoldalagi/C0_LID_DEV", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ntoldalagi/C0_LID_DEV: direct link, hf CLI and curl.
- Browser
- Download file 2.8 kB
-
https://huggingface.co/ntoldalagi/C0_LID_DEV/resolve/main/training_args.bin
- Command line
-
hf download hf://ntoldalagi/C0_LID_DEV/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ntoldalagi/C0_LID_DEV/resolve/main/training_args.bin
2.8 kB
- Xet hash:
- 1f37c8bd4ee5aaa3197dc900a63621f16a51f034df3f50045969025b8e7e1d3c
- Size of remote file:
- 2.8 kB
- SHA256:
- 4465908f60547c85cba842e87484b07efbf0f6622bc3a1333701adff4fd7854b
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