Instructions to use ks2303/tts-transformer-ljspeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ks2303/tts-transformer-ljspeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="ks2303/tts-transformer-ljspeech")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ks2303/tts-transformer-ljspeech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 217062656f1aeab81c625b31d3ce0f582b0ade4f81a287f4a5873d86f9893995
- Size of remote file:
- 83.4 MB
- SHA256:
- 0dc473412bd8d378a009816886e34686746f1e603ba1f34a5279018f26f0fc7d
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