Add pipeline tag, paper link, and citation to model card

README.md

@@ -1,24 +1,24 @@
 ---
-library_name: transformers
-license: cc-by-nc-sa-4.0
-language:
-- en
 base_model:
 - ryota-komatsu/SylReg-Decoder-Base
+language:
+- en
+library_name: transformers
+license: cc-by-nc-sa-4.0
+pipeline_tag: text-to-speech
 ---
 
 # SylReg-Decoder
 
-<!-- Provide a quick summary of what the model is/does. -->
-
+This repository contains the decoder model presented in the paper [Speaker-Disentangled Chunk-Wise Regression for Syllabic Tokenization](https://huggingface.co/papers/2607.04064).
 
+- **Project Page:** https://ryota-komatsu.github.io/speaker_disentangled_hubert
+- **Repository:** https://github.com/ryota-komatsu/speaker_disentangled_hubert
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
 - **Model type:** Flow-matching-based Diffusion Transformer (DiT) with BigVGAN-v2
 - **Language(s) (NLP):** English
 - **License:** CC BY-NC-SA 4.0
@@ -26,8 +26,6 @@ base_model:
 
 ### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
 - **Repository:** [Code](https://github.com/ryota-komatsu/speaker_disentangled_hubert)
 - **Demo:** [Project page](https://ryota-komatsu.github.io/speaker_disentangled_hubert)
 
@@ -80,8 +78,6 @@ generated_speech = decoder(units.unsqueeze(0)).waveform.cpu()
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
 |  | License | Provider |
 | --- | --- | --- |
 | [LibriTTS-R](https://www.openslr.org/141/) | CC BY 4.0 | Y. Koizumi *et al.* |
@@ -93,4 +89,17 @@ generated_speech = decoder(units.unsqueeze(0)).waveform.cpu()
 
 ## Hardware
 
-2 x A6000
+2 x A6000
+
+## Citation
+
+```bibtex
+@article{Komatsu_SylReg_2026,
+  author    = {Komatsu, Ryota and Kawakita, Kota and Okamoto, Takuma and Shinozaki, Takahiro},
+  title     = {Speaker-Disentangled Chunk-Wise Regression for Syllabic Tokenization},
+  year      = {2026},
+  volume    = {7},
+  journal   = {IEEE Open Journal of Signal Processing},
+  pages     = {},
+}
+```