Instructions to use rossevine/Model_G_P with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rossevine/Model_G_P with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rossevine/Model_G_P")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rossevine/Model_G_P") model = AutoModelForCTC.from_pretrained("rossevine/Model_G_P", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15f4097988cc52711addf340b966debf2b677be66cdb5100701f00a1e793a8b1
- Size of remote file:
- 3.96 kB
- SHA256:
- 9bf77272bb0ab7ce45de47c6cbfe8daedbdfb136a5b25eddd6b40a1462b33d7a
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