upd md

README.md

@@ -107,7 +107,7 @@ configs:
 # Dataset Card for Acapella Evaluation
 The original dataset, sourced from the [Acapella Evaluation Dataset](https://ccmusic-database.github.io/en/database/ccm.html#shou2), comprises six Mandarin pop song segments performed by 22 singers, resulting in a total of 132 audio clips. Each segment includes both a verse and a chorus. Four judges from the China Conservatory of Music assess the singing across nine dimensions: pitch, rhythm, vocal range, timbre, pronunciation, vibrato, dynamics, breath control, and overall performance, using a 10-point scale. The evaluations are recorded in an Excel spreadsheet in .xls format.
 
-Due to the original dataset comprising separate files for audio recordings and evaluation sheets, which hindered efficient data retrieval, we combined the original vocal recordings with their corresponding evaluation sheets to construct the [default subset](#usage) of the current integrated version of the dataset. The data structure can be viewed in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/acapella/dataPeview). The current dataset is already endorsed by published articles, hence there is no need to construct the eval subset.
+Due to the original dataset comprising separate files for audio recordings and evaluation sheets, which hindered efficient data retrieval, we combined the original vocal recordings with their corresponding evaluation sheets to construct the [default subset](#usage) of the current integrated version of the dataset. The data structure can be viewed in the [viewer](https://huggingface.co/datasets/ccmusic-database/acapella/viewer). The current dataset is already endorsed by published articles, hence there is no need to construct the eval subset.
 
 ## Dataset Structure
 <style>
@@ -157,13 +157,13 @@ Chinese, English
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset(
+ds = load_dataset(
     "ccmusic-database/acapella",
     name="default",
     cache_dir="./__pycache__",
 )
 for i in range(1, 7):
-    for item in dataset[f"song{i}"]:
+    for item in ds[f"song{i}"]:
         print(item)
 ```