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:
- 43c1cb96ee5be22dbb799f73719d774e24388716d29886b706fb8f59916c0a8f
- Size of remote file:
- 1.26 GB
- SHA256:
- ba951c057ae78e0a6259a96ead51e6670a40b34946a9f3e5a76a62fff08917e8
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