upd md

.gitignore

@@ -1,3 +1,2 @@
-rename.sh
 test.*
 *__pycache__*

README.md

@@ -122,7 +122,7 @@ This dataset is created and used by [[1]](https://arxiv.org/pdf/2303.13272) for
 ## Integration
 In the original dataset, the labels were stored in a separate CSV file. This posed usability challenges, as researchers had to perform time-consuming operations on CSV parsing and label-audio alignment. After our integration, the data structure has been streamlined and optimized. It now contains three columns: audio sampled at 44,100 Hz, pre-processed mel spectrograms, and a dictionary. This dictionary contains onset, offset, technique numeric labels, and pitch. The number of data entries after integration remains 99, with a cumulative duration amounting to 151.08 minutes. The average audio duration is 91.56 seconds.
 
-We performed data processing and constructed the [default subset](#default-subset) of the current integrated version of the dataset, and the details of its data structure can be viewed through the [viewer](https://huggingface.co/datasets/ccmusic-database/Guzheng_Tech99/viewer). In light of the fact that the current dataset has been referenced and evaluated in a published article, we transcribe here the details of the dataset processing during the evaluation in the said article: each audio clip is a 3-second segment sampled at 44,100Hz, which is then converted into a log Constant-Q Transform (CQT) spectrogram. A CQT accompanied by a label constitutes a single data entry, forming the first and second columns, respectively. The CQT is a 3-dimensional array with dimensions of 88x258x1, representing the frequency-time structure of the audio. The label, on the other hand, is a 2-dimensional array with dimensions of 7x258, indicating the presence of seven distinct techniques across each time frame. Ultimately, given that the original dataset has already been divided into train, valid, and test sets, we have integrated the feature extraction method mentioned in this article's evaluation process into the API, thereby constructing the [eval subset](#eval-subset), which is not embodied in our paper.
+We performed data processing and constructed the [default subset](#usage) of the current integrated version of the dataset, and the details of its data structure can be viewed through the [viewer](https://huggingface.co/datasets/ccmusic-database/Guzheng_Tech99/viewer). In light of the fact that the current dataset has been referenced and evaluated in a published article, we transcribe here the details of the dataset processing during the evaluation in the said article: each audio clip is a 3-second segment sampled at 44,100Hz, which is then converted into a log Constant-Q Transform (CQT) spectrogram. A CQT accompanied by a label constitutes a single data entry, forming the first and second columns, respectively. The CQT is a 3-dimensional array with dimensions of 88x258x1, representing the frequency-time structure of the audio. The label, on the other hand, is a 2-dimensional array with dimensions of 7x258, indicating the presence of seven distinct techniques across each time frame. Ultimately, given that the original dataset has already been divided into train, valid, and test sets, we have integrated the feature extraction method mentioned in this article's evaluation process into the API, thereby constructing the [eval subset](#usage), which is not embodied in our paper.
 
 ## Statistics
 In this part, we present statistics at the label-level. The number of audio clips is equivalent to the count of either onset or offset occurrences. The duration of an audio clip is determined by calculating the offset time minus the onset time. At this level, the number of clips is 15,838, and the total duration is 162.69 minutes. 
@@ -170,34 +170,17 @@ MIR, audio frame-level detection, Guzheng playing technique detection
 Chinese, English
 
 ## Usage
-### Default Subset
 ```python
 from datasets import load_dataset
 
-ds = load_dataset("ccmusic-database/Guzheng_Tech99", name="default")
-for item in ds["train"]:
-    print(item)
-
-for item in ds["validation"]:
-    print(item)
-
-for item in ds["test"]:
-    print(item)
-```
-
-### Eval Subset
-```python
-from datasets import load_dataset
-
-ds = load_dataset("ccmusic-database/Guzheng_Tech99", name="eval")
-for item in ds["train"]:
-    print(item)
-
-for item in ds["validation"]:
-    print(item)
-
-for item in ds["test"]:
-    print(item)
+ds = load_dataset(
+    "ccmusic-database/Guzheng_Tech99",
+    name="default",  # default / eval
+    split="train",  # train / validation / test
+    cache_dir="./__pycache__",
+)
+for i in ds:
+    print(i)
 ```
 
 ## Maintenance