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:
- 17be913f681646dc2ade14bf154b7cc3f8e9cfef2d15d42571149cb60f80762a
- Size of remote file:
- 17.7 kB
- SHA256:
- cd86cb193cb39a077a2cb2540af2953ce7f2f480ec3129c5191e730b87329493
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