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README.md

@@ -1,35 +1,25 @@
 ---
 license: cc-by-nc-4.0
-task_categories:
-- text-to-audio
-language:
-- en
 tags:
-- music
-- chinese-traditional
 - benchmark
+- music-generation
+- evaluation
 size_categories:
-- 100M<n<1G
+- 1G<n<10G
 ---
 
-# CTIM-Bench: A Benchmark for Traditional Chinese Instrument Music Generation
+# CTIM-Bench: Benchmark for Traditional Chinese Music Generation
 
-This dataset serves as the official evaluation benchmark for the **CTIM-Gen** project (Submitted to ICMR 2026).
+**CTIM-Bench** is a curated evaluation dataset consisting of 500 text-audio pairs, specifically designed to benchmark music generation models on traditional Chinese instruments. It serves as the test set for the **CTIM-Gen** project.
 
-## Overview
+## ⚖️ Data Source & Usage Policy
 
-**CTIM-Bench** contains **500 high-quality audio samples** selected to evaluate the generation quality of traditional Chinese instruments. It is used to compute metrics such as FAD, KL Divergence, and CLAP Score.
+The audio data in this benchmark was collected from [China Music Network (www.china1901.com)](http://www.china1901.com/).
 
-## Structure
+**Important**: This dataset is provided **solely for academic research purposes** in the field of AI music generation. Commercial use is strictly prohibited. By downloading or using this dataset, you agree to these terms.
 
-- **`audio/`**: Contains 500 `.wav` files (e.g., `yangqin0007_s019.wav`).
-- **`metadata.json`**: Contains the text prompts and instrument labels for each sample.
+## Contents
 
-## Usage
-
-This benchmark is designed to be used with the evaluation scripts provided in the [CTIM-Gen Model Repository](https://huggingface.co/CTIM-Gen/CTIM-Gen).
-
-```python
-# Example prompt from metadata.json
-"A performance of yangqin, featuring bright and metallic tone, traditional Chinese folk music style, fast tempo."
-```
+- **audio/**: Ground truth audio files (30s clips).
+- **test_prompts.json**: Metadata containing prompts and file references.
+- **Instruments**: Guqin, Guzheng, Matouqin, Hulusi, Pipa.