Instructions to use voices/VCTK_European_English_Males with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voices/VCTK_European_English_Males with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="voices/VCTK_European_English_Males")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("voices/VCTK_European_English_Males", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 17fdc35f08825f916afbedaae3d2e25c24f42330331ae7a8a089e61a4efb9ab1
- Size of remote file:
- 1.04 GB
- SHA256:
- 9a35efbe123ea3f06a4309417c93edfae5adae29090c7c6048522e149351a79e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.