Instructions to use reach-vb/bark-endpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reach-vb/bark-endpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="reach-vb/bark-endpoint")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("reach-vb/bark-endpoint") model = AutoModelForTextToWaveform.from_pretrained("reach-vb/bark-endpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from reach-vb/bark-endpoint: direct link, hf CLI and curl.
- Browser
- Download file 4.49 GB
-
https://huggingface.co/reach-vb/bark-endpoint/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://reach-vb/bark-endpoint/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/reach-vb/bark-endpoint/resolve/main/pytorch_model.bin
4.49 GB
- Xet hash:
- e106af7d7f68140ebb3a2d198cdffcb4c03e8dbed5749e3ff9b92b629fc14bd9
- Size of remote file:
- 4.49 GB
- SHA256:
- 4e3d407b9b3b619da184c85786c88e5e35f90f9089303e16db696ed0be477989
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