Improve CSEMOTIONS dataset card: Add metadata and links

README.md

@@ -1,28 +1,42 @@
 ---
+task_categories:
+- text-to-speech
+library_name: datasets
+license: apache-2.0
+language:
+- zh
+tags:
+- speech
+- emotional-speech
+- voice-cloning
+- mandarin
 dataset_info:
   features:
-    - name: audio
-      dtype: audio
-    - name: text
-      dtype: string
-    - name: emotion
-      dtype: string
-    - name: speaker
-      dtype: string
+  - name: audio
+    dtype: audio
+  - name: text
+    dtype: string
+  - name: emotion
+    dtype: string
+  - name: speaker
+    dtype: string
   splits:
-    - name: train
-      num_bytes: 3610108297.64
-      num_examples: 4160
+  - name: train
+    num_bytes: 3610108297.64
+    num_examples: 4160
   download_size: 3077432286
   dataset_size: 3610108297.64
 configs:
-  - config_name: default
-    data_files:
-      - split: train
-        path: data/train-*
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 ---
+
 # CSEMOTIONS: High-Quality Mandarin Emotional Speech Dataset
 
+[Paper](https://huggingface.co/papers/2508.02038) | [Code](https://github.com/AIDC-AI/Marco-Voice)
+
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
 
 **CSEMOTIONS** is a high-quality Mandarin emotional speech dataset designed for expressive speech synthesis, emotion recognition, and voice cloning research. The dataset contains studio-quality recordings from six professional voice actors across seven carefully curated emotional categories, supporting research in controllable and natural language speech generation.
@@ -32,7 +46,7 @@ configs:
 
 - **Name:** CSEMOTIONS
 - **Total Duration:** ~10 hours
-- **Speakers:** 10 (5 male, 5 female) native Mandarin speakers, all professional voice actors
+- **Speakers:** 6 (3 male, 3 female) native Mandarin speakers, all professional voice actors
 - **Emotions:** Neutral, Happy, Angry, Sad, Surprise, Disgust, Fear
 - **Language:** Mandarin Chinese
 - **Sampling Rate:** 48kHz, 24-bit PCM
@@ -118,4 +132,4 @@ The project is licensed under the Apache License 2.0 (http://www.apache.org/lice
                                      
 We used compliance checking algorithms during the training process, to ensure the compliance of the trained model and dataset to the best of our ability. Due to the complexity of the data and the diversity of language model usage scenarios, we cannot guarantee that the dataset is completely free of copyright issues or improper content. If you believe anything infringes on your rights or contains improper content, please contact us, and we will promptly address the matter.
                                      
----
+---