Instructions to use ylacombe/bark-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ylacombe/bark-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="ylacombe/bark-large")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("ylacombe/bark-large") model = AutoModelForTextToWaveform.from_pretrained("ylacombe/bark-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7571ed84e222b3e964d40e68ab6aa4ee88bdf6dab0e8fa7b344c7454c6419a41
- Size of remote file:
- 17.1 kB
- SHA256:
- 229455388c628e9d168ad11940041b15ed3b40f724ab3a4747f6cb38b2f5230a
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