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MOSS-TTSD-v1.0

feature-extraction

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rulerman commited on 13 days ago

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update README only

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@@ -258,8 +258,11 @@ We introduce a robust evaluation framework leveraging **MMS-FA** for alignment a
 ### Subjective Evaluation
 For open-source models, annotators are asked to score each sample pair in terms of speaker attribution accuracy, voice similarity, prosody, and overall quality. Following the methodology of the LMSYS Chatbot Arena, we compute Elo ratings and confidence intervals for each dimension.
-![alt text](assets/VS_Open-Source_Models.png)
 For closed-source models, annotators are only asked to choose the overall preferred one in each pair, and we compute the win rate accordingly.
-![alt text](assets/VS_Proprietary_Models1.png)
-![alt text](assets/VS_Proprietary_Models2.png)

 ### Subjective Evaluation
 For open-source models, annotators are asked to score each sample pair in terms of speaker attribution accuracy, voice similarity, prosody, and overall quality. Following the methodology of the LMSYS Chatbot Arena, we compute Elo ratings and confidence intervals for each dimension.
+<p align="center">
+  <img src="https://speech-demo.oss-cn-shanghai.aliyuncs.com/moss_tts_demo/tts_readme_imgaes_demo/moss_ttsd_subjective_evaluation" width="85%" />
+</p>
 For closed-source models, annotators are only asked to choose the overall preferred one in each pair, and we compute the win rate accordingly.
+<p align="center">
+  <img src="https://speech-demo.oss-cn-shanghai.aliyuncs.com/moss_tts_demo/tts_readme_imgaes_demo/moss_ttsd_winrate" width="85%" />
+</p>