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:
- 54c56f27d62df6efd21fd1702579d40b82c653d1b0d9348274e12217d8594357
- Size of remote file:
- 20.6 kB
- SHA256:
- 39ca19ff531b1dc16f1d16f0736cd69214a8fba7a8c1ca91edae9bf0aea3ce66
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