Instructions to use ms3c/model-output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ms3c/model-output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ms3c/model-output")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("ms3c/model-output") model = AutoModelForCTC.from_pretrained("ms3c/model-output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 150ec9e5c3be2d5ed82b4470da91ac56baae13ab009dd6283fcb0e3239344d22
- Size of remote file:
- 1.26 GB
- SHA256:
- 87b96ded44d435d64a9e362590393fdadbb06a413fd0950760b5fb4f3a63d9bf
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