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:
- ccfffc6b7477090ab237681fe1f4e244899c19b1604fabc06f129c09702c7a23
- Size of remote file:
- 3.96 kB
- SHA256:
- 2955c520fb7db59c99e19a925daddf9e3f941f1989e03af28b84fdfeb57f2a22
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