Instructions to use nnaik39/content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nnaik39/content with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nnaik39/content")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("nnaik39/content") model = AutoModelForCTC.from_pretrained("nnaik39/content", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7abf407e9f8c5c70bc35de579582b5ba02a7ede091389676b21fe7305042e068
- Size of remote file:
- 36.5 MB
- SHA256:
- 1ef64781aa03180f4f5ce504314f058f5d0227277df86060473d973cf43b033e
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