Instructions to use Matthijs/vits-ljs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Matthijs/vits-ljs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Matthijs/vits-ljs")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("Matthijs/vits-ljs") model = AutoModelForTextToWaveform.from_pretrained("Matthijs/vits-ljs") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f285382d8b1444f478dfe36c336d03d4dacde56bd9c0553a69e0e331fd2bc51
- Size of remote file:
- 145 MB
- SHA256:
- 07020fce7d9c74715668f4bddbfc7e7dd127c2ab3cbe3b74247536eee18b86e6
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