Instructions to use CUAIStudents/DeepAr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CUAIStudents/DeepAr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="CUAIStudents/DeepAr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("CUAIStudents/DeepAr") model = AutoModelForSpeechSeq2Seq.from_pretrained("CUAIStudents/DeepAr") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0cf66b7b6109d750d9ff5475a96659cd0a7e8330f975a22991c026fb15af5db
- Size of remote file:
- 3.24 GB
- SHA256:
- 5f7b9b9a375e8c8e5849855c4a36e11b842ac96c8291ef006b9e553d0240edeb
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