Instructions to use ngohuudang/test_bug2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ngohuudang/test_bug2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ngohuudang/test_bug2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("ngohuudang/test_bug2") model = AutoModelForCTC.from_pretrained("ngohuudang/test_bug2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5c1bbc0c69226a03e06e7d9a96771b93adaf432933ea3c531506e4fe6a150a7
- Size of remote file:
- 3 kB
- SHA256:
- 9c7fb130b6f6218a56af05243a0566d2d763724e5a7c232862c7d90f41759eb6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.