Instructions to use suno/bark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suno/bark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="suno/bark")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("suno/bark") model = AutoModelForTextToWaveform.from_pretrained("suno/bark") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 35c439dd8cddfc6909f6ac07ca77836bde6fae4b1f5a952f12bcb3988c53f35e
- Size of remote file:
- 1.25 GB
- SHA256:
- 110580140ce5319b5b26849e24378d7594eb75ad11e7203e3091a876a07e4536
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.