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:
- 19dd78cab2e77a6471f38f07c7ae0f855e4202919aa323c21657891bdf0d0ae2
- Size of remote file:
- 5.98 kB
- SHA256:
- 0268e63ef8aa7c6113af4a840c3581c08edea0eacec8aaa7d52da8012d8c16f5
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