Update README.md

README.md

@@ -25,7 +25,7 @@ configs:
 
 ## About Audio Turing Test (ATT)
 
-ATT is an evaluation framework with a standardized human evaluation protocol and an accompanying dataset, aiming to resolve the lack of unified protocols in TTS evaluation and the difficulty in comparing multiple TTS systems. To further support the training and iteration of TTS systems, we utilized additional private evaluation data to train Auto-ATT model based on Qwen2-Audio-7B, enabling a model-as-a-judge approach for rapid evaluation of TTS systems on the ATT dataset. The datasets and Auto-ATT model can be cound in [ATT Collection](https://huggingface.co/collections/AudioTuring/audio-turing-test-6826e24d2197bf91fae6d7f5).
+ATT is an evaluation framework with a standardized human evaluation protocol and an accompanying dataset, aiming to resolve the lack of unified protocols in TTS evaluation and the difficulty in comparing multiple TTS systems. To further support the training and iteration of TTS systems, we utilized additional private evaluation data to train Auto-ATT model based on Qwen2-Audio-7B, enabling a model-as-a-judge approach for rapid evaluation of TTS systems on the ATT dataset. The datasets and Auto-ATT model can be cound in [ATT Collection](https://huggingface.co/collections/meituan/audio-turing-test-682446320368164faeaf38a4).
 
 ## Dataset Description
 
@@ -53,8 +53,8 @@ Additionally, the dataset includes 104 "trap" transcripts for attentiveness chec
 ## How to Use This Dataset
 
 1. **Generate Speech:** Use these transcripts to generate audio with your TTS model. Pay attention that here are some phone numbers, email addresses, and websites in the corpus. Due to potential sensitivity risks, we have masked these texts as placeholders: [PHONE_MASK], [EMAIL_MASK], and [WEB_MASK]. However, to properly test the TTS system’s capabilities in this regard, please replace these placeholders with actual content before use.
-2. **Evaluate:** Use our [Auto-ATT evaluation model](https://huggingface.co/AudioTuring/Auto-ATT) to score your generated audio.
-3. **Benchmark:** Compare your model’s scores against scores from other evaluated TTS models listed in our research paper and the "trap" audio clips in [Audio Turing Test Audio](https://huggingface.co/collections/AudioTuring/audio-turing-test-6826e24d2197bf91fae6d7f5).
+2. **Evaluate:** Use our [Auto-ATT evaluation model](https://huggingface.co/Meituan/Auto-ATT) to score your generated audio.
+3. **Benchmark:** Compare your model’s scores against scores from other evaluated TTS models listed in our research paper and the "trap" audio clips in [Audio Turing Test Audio](https://huggingface.co/collections/Meituan/audio-turing-test-6826e24d2197bf91fae6d7f5).
 
 
 ## Data Format
@@ -95,7 +95,7 @@ This dataset is openly accessible for research purposes. If you use this dataset
   author = {Wang, Xihuai and Zhao, Ziyi and Ren, Siyu and Zhang, Shao and Li, Song and Li, Xiaoyu and Wang, Ziwen and Qiu, Lin and Wan, Guanglu and Cao, Xuezhi and Cai, Xunliang and Zhang, Weinan},
   title = {Audio Turing Test: Benchmarking the Human-likeness and Naturalness of Large Language Model-based Text-to-Speech Systems in Chinese},
   year = {2025},
-  url = {https://huggingface.co/AudioTuring/Audio-Turing-Test-Transcripts},
+  url = {https://huggingface.co/Meituan/Audio-Turing-Test-Corpus},
   publisher = {huggingface},
 }
 ```