Instructions to use amphion/TaDiCodec-TTS-MGM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amphion/TaDiCodec-TTS-MGM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="amphion/TaDiCodec-TTS-MGM")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("amphion/TaDiCodec-TTS-MGM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Improve model card: Add paper title, abstract, and project page
Verified signatureThis PR enhances the model card by making the paper title a prominent heading, adding the full abstract for comprehensive context, and including an explicit "Project Page" section. These additions improve the discoverability and completeness of the model's documentation for users on the Hugging Face Hub. All existing metadata (pipeline tag, library name, license, languages, and other tags) remains unchanged as it is already correctly defined. The sample usage code is also preserved, as it is accurately reflected from the GitHub repository.df533ba
-
Add `library_name: transformers` to metadata (#1)
Verified signature- Add `library_name: transformers` to metadata (cba53796839747ee5cf000679e36c1bbbb5f7fa9) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>ce635a7 -
Update README.md
Verified signaturea4df660
-
Update README.md
Verified signature9797258 -
Upload folder using huggingface_hub
Verified signature6b3a1f6 -
initial commit
Verified signature6fe8951