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 pytorch_model.bin from ntoldalagi/C0_LID_DEV: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/ntoldalagi/C0_LID_DEV/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ntoldalagi/C0_LID_DEV/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ntoldalagi/C0_LID_DEV/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- a9655d9579fd2932716fc3e064534c9ee5bc30e0d622d76eaf0041218f782ef5
- Size of remote file:
- 1.26 GB
- SHA256:
- 8ad7128d306bf0f6b6c37d41699d6a3e6bd37ba3727993194c970c101d53a75c
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