Instructions to use ICTNLP/SLED-TTS-Libriheavy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ICTNLP/SLED-TTS-Libriheavy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="ICTNLP/SLED-TTS-Libriheavy")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ICTNLP/SLED-TTS-Libriheavy", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e278adffac0fafc173a02fcf509edd186e375b7a9c54b757bdab11b5140fcf10
- Size of remote file:
- 815 MB
- SHA256:
- d7aa1202402331d0db37b3a5775fc8f1db0b0169192986f511381ceec6e2529d
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