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:
- 7649956b04edc8a5e69f2974ec67be9dd36a8cd0339bff29bc49abdcf994e0f4
- Size of remote file:
- 10 kB
- SHA256:
- 04018345b2fe98c5a5d86dad0931f8ddad010307c3d3fd3eecc7c89a168747eb
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