Instructions to use artificial-feelings/bark-forked with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use artificial-feelings/bark-forked with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="artificial-feelings/bark-forked")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("artificial-feelings/bark-forked") model = AutoModelForTextToWaveform.from_pretrained("artificial-feelings/bark-forked", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2aeb65ac697703bbaef1fb985c8e0fb0af03a1bb4285d5859111eb8712ac02fe
- Size of remote file:
- 9.47 kB
- SHA256:
- 6ac93157e39cd0fa7a5e9d0404d6adcb3daaec8e820b42b72b96de62e19ca89b
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