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:
- 164bf72c51d2e47c2df0a81bf3257c851788fb7dbe7a1ce0a7a8da18254e917a
- Size of remote file:
- 6.94 kB
- SHA256:
- fd5e9d07b0480dda2c3661de1da0671f34419c029c8e7410705139a38fb133d3
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