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:
- 2ef572b61550ad188af7ad6f76409a98521b54d2313a6456492007c47f8f88c7
- Size of remote file:
- 2.32 GB
- SHA256:
- ecd798cf39a5ecbec30ef41a3d9d63fb61ea09b78d3bd5dfebb2f7343087b1be
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.