Instructions to use ryota-komatsu/SylReg-Decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ryota-komatsu/SylReg-Decoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="ryota-komatsu/SylReg-Decoder")# Load model directly from transformers import FlowMatchingWithBigVGan model = FlowMatchingWithBigVGan.from_pretrained("ryota-komatsu/SylReg-Decoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag, paper link, and citation to model card
#1
by nielsr HF Staff - opened
This PR improves the model card for SylReg-Decoder by:
- Adding the
pipeline_tag: text-to-speechto the YAML metadata to ensure the model is categorized correctly and is easily discoverable. - Adding a link to the research paper Speaker-Disentangled Chunk-Wise Regression for Syllabic Tokenization in the markdown.
- Adding the official BibTeX citation for the paper.
ryota-komatsu changed pull request status to merged