Instructions to use cwkeam/m-ctc-t-large-lid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cwkeam/m-ctc-t-large-lid with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import MCTCTForAudioFrameClassification model = MCTCTForAudioFrameClassification.from_pretrained("cwkeam/m-ctc-t-large-lid", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cwkeam/m-ctc-t-large-lid: direct link, hf CLI and curl.
- Browser
- Download file 4.19 GB
-
https://huggingface.co/cwkeam/m-ctc-t-large-lid/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cwkeam/m-ctc-t-large-lid/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cwkeam/m-ctc-t-large-lid/resolve/main/pytorch_model.bin
4.19 GB
- Xet hash:
- 1e6f4d524904c777d172fd25137e801c06d7a24afc74f58fd6420f6cbbbcb365
- Size of remote file:
- 4.19 GB
- SHA256:
- 4ebe460f70052e0f0b8c1039e11b9bb0a9e2c135fb98b8e88562e5a5936d073f
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