Instructions to use rossevine/Model_G_S_Berita_Wav2Vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rossevine/Model_G_S_Berita_Wav2Vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rossevine/Model_G_S_Berita_Wav2Vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rossevine/Model_G_S_Berita_Wav2Vec2") model = AutoModelForCTC.from_pretrained("rossevine/Model_G_S_Berita_Wav2Vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8303674526f2e0265e5552db03706901e47692e41de1f47b984db9b47f3b6f3c
- Size of remote file:
- 2.86 kB
- SHA256:
- b17b0bd9ef1836994ad356297356c017b1e9220392b66352af0352899cecdd5e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.