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:
- 94863afaca2ae7570680d4fdb11377a711cbfd095a02e2344728b88a37d8bc41
- Size of remote file:
- 4.54 kB
- SHA256:
- d02fa6b88b905fab1ad5afeddc3ab79d412b29b51f6be7ccfbf9e2187b05028b
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