Instructions to use kakao-enterprise/vits-vctk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kakao-enterprise/vits-vctk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="kakao-enterprise/vits-vctk")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("kakao-enterprise/vits-vctk") model = AutoModelForTextToWaveform.from_pretrained("kakao-enterprise/vits-vctk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 379f619044a40a5926d0696a7a159f70696ca70fbff2ca9b3dd1e68bb285c4c9
- Size of remote file:
- 159 MB
- SHA256:
- 99c1afe89af5d9e865a8ff42d747ef71c2310db17a824a741227f126a150ecd0
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