Instructions to use DeepLoading/slang-stt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepLoading/slang-stt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DeepLoading/slang-stt")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DeepLoading/slang-stt") model = AutoModelForCTC.from_pretrained("DeepLoading/slang-stt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c37fedcebd7bc2c3487e62d6bde4978d7a94b9ac0e447fa7b5f5b70cd436473
- Size of remote file:
- 3.07 kB
- SHA256:
- bd18ef123f5c51fa2f4eb8292ce648dfb1f4b4b83fd146fba2117c260ebc704c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.