Update README.md

models/tts/maskgct/README.md

@@ -17,7 +17,7 @@ MaskGCT (**Mask**ed **G**enerative **C**odec **T**ransformer) is *a fully non-au
 
 ## News
 
-- **2024/10/19**: We release **MaskGCT**, a fully non-autoregressive TTS model that eliminates the need for explicit alignment information between text and speech supervision. MaskGCT is trained on [Emilia](../../../preprocessors/Emilia) dataset and achieves SOTA zero-shot TTS perfermance.
+- **2024/10/19**: We release **MaskGCT**, a fully non-autoregressive TTS model that eliminates the need for explicit alignment information between text and speech supervision. MaskGCT is trained on [Emilia](https://huggingface.co/datasets/amphion/Emilia-Dataset) dataset and achieves SOTA zero-shot TTS perfermance.
 
 ## Quickstart
 
@@ -136,7 +136,7 @@ We also provide a [jupyter notebook](../../../models/tts/maskgct/maskgct_demo.ip
 
 ## Training Dataset
 
-We use the [Emilia](../../../preprocessors/Emilia) dataset to train our models. Emilia is a multilingual and diverse in-the-wild speech dataset designed for large-scale speech generation. In this work, we use English and Chinese data from Emilia, each with 50K hours of speech (totaling 100K hours).
+We use the [Emilia](https://huggingface.co/datasets/amphion/Emilia-Dataset) dataset to train our models. Emilia is a multilingual and diverse in-the-wild speech dataset designed for large-scale speech generation. In this work, we use English and Chinese data from Emilia, each with 50K hours of speech (totaling 100K hours).
 
 
 ## Evaluation Results of MaskGCT