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:
- 61867ecc5da3fe8c3eae4f64044c4c4bf5821b7b8863f10162863d3ecc782cd5
- Size of remote file:
- 1.26 GB
- SHA256:
- 164d5281cc0c57bd107c31501e824e381526e3fc798f36116129be4cc08fe7df
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