Instructions to use BasketTechologies/bark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BasketTechologies/bark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="BasketTechologies/bark")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("BasketTechologies/bark") model = AutoModelForTextToWaveform.from_pretrained("BasketTechologies/bark") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e106af7d7f68140ebb3a2d198cdffcb4c03e8dbed5749e3ff9b92b629fc14bd9
- Size of remote file:
- 4.49 GB
- SHA256:
- 4e3d407b9b3b619da184c85786c88e5e35f90f9089303e16db696ed0be477989
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