sync ms

README.md

@@ -15,90 +15,89 @@ size_categories:
 - n<1K
 viewer: false
 ---
+
 # Dataset Card for Acapella Evaluation
+This raw dataset 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.
 
 ## Dataset Description
-
 - **Homepage:** <https://ccmusic-database.github.io>
 - **Repository:** <https://huggingface.co/datasets/CCMUSIC/acapella_evaluation>
 - **Paper:** <https://doi.org/10.5281/zenodo.5676893>
-- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
+- **Leaderboard:** <https://www.modelscope.cn/datasets/ccmusic/acapella>
 - **Point of Contact:** <https://www.mdpi.com/2076-3417/12/19/9931>
 
 ### Dataset Summary
-
-This database contains 6 Mandarin song segments sung by 22 singers, totaling 132 audio clips. Each segment consists of a verse and a chorus. Four judges evaluate the singing from nine aspects which are pitch, rhythm, vocal range, timbre, pronunciation, vibrato, dynamic, breath control and overall performance on a 10-point scale. The scores are recorded in a sheet.
+Due to the original dataset comprising separate files for audio recordings and evaluation sheets, which hindered efficient data retrieval, we have consolidated the raw vocal recordings with their corresponding assessments. The dataset is divided into six segments, each representing a different song, resulting in a total of six divisions. Each segment contains 22 entries, with each entry detailing the vocal recording of an individual singer sampled at 22,050 Hz, the singer's ID, and evaluations across the nine dimensions previously mentioned. Consequently, each entry encompasses 11 columns of data. This dataset is well-suited for tasks such as vocal analysis and regression-based singing voice rating. For instance, as previously stated, the final column of each entry denotes the overall performance score, allowing the audio to be utilized as data and this score to serve as the label for regression analysis.
 
 ### Supported Tasks and Leaderboards
-
 Acapella evaluation/scoring
 
 ### Languages
-
 Chinese, English
 
+## Maintenance
+```bash
+GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/acapella
+cd music_genre
+```
+
+## Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ccmusic-database/acapella")
+for i in range(1, 7):
+    for item in dataset[f"song{i}"]:
+        print(item)
+```
+
 ## Dataset Structure
+|                    audio(22050Hz)                    |           mel(22050Hz)            | singer_id | pitch / rhythm / ... / overall_performance |
+| :--------------------------------------------------: | :-------------------------------: | :-------: | :----------------------------------------: |
+| <audio controls src="./data/song1 (16).wav"></audio> | <img src="./data/song1 (16).jpg"> |    int    |                float(0-10)                 |
 
 ### Data Instances
-
 .wav & .csv
 
 ### Data Fields
-
 song, singer id, pitch, rhythm, vocal range, timbre, pronunciation, vibrato, dynamic, breath control and overall performance
 
 ### Data Splits
-
 song1-6
 
 ## Dataset Creation
-
 ### Curation Rationale
-
-Lack of a training dataset for acapella scoring system
+Lack of a training dataset for the acapella scoring system
 
 ### Source Data
-
 #### Initial Data Collection and Normalization
-
 Zhaorui Liu, Monan Zhou
 
 #### Who are the source language producers?
-
 Students and judges from CCMUSIC
 
 ### Annotations
-
 #### Annotation process
-
-6 Mandarin song segments were sung by 22 singers, totaling 132 audio clips. Each segment consists of a verse and a chorus. Four judges evaluate the singing from nine aspects which are pitch, rhythm, vocal range, timbre, pronunciation, vibrato, dynamic, breath control and overall performance on a 10-point scale. The scores are recorded in a sheet.
+6 Mandarin song segments were sung by 22 singers, totaling 132 audio clips. Each segment consists of a verse and a chorus. Four judges evaluate the singing from nine aspects which are pitch, rhythm, vocal range, timbre, pronunciation, vibrato, dynamic, breath control and overall performance on a 10-point scale. The scores are recorded on a sheet.
 
 #### Who are the annotators?
-
 Judges from CCMUSIC
 
 ### Personal and Sensitive Information
-
 Singers' and judges' names are hided
 
 ## Considerations for Using the Data
-
 ### Social Impact of Dataset
-
-Providing a training dataset for acapella scoring system may improve the developement of related Apps
+Providing a training dataset for the acapella scoring system may improve the development of related Apps
 
 ### Discussion of Biases
-
 Only for Mandarin songs
 
 ### Other Known Limitations
-
 No starting point has been marked for the vocal
 
 ## Additional Information
-
 ### Dataset Curators
-
 Zijin Li
 
 ### Evaluation
@@ -130,18 +129,16 @@ SOFTWARE.
 ```
 
 ### Citation Information
-```
+```bibtex
 @dataset{zhaorui_liu_2021_5676893,
-  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li},
-  title        = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
-  month        = {nov},
-  year         = {2021},
-  publisher    = {Zenodo},
-  version      = {1.1},
-  doi          = {10.5281/zenodo.5676893},
-  url          = {https://doi.org/10.5281/zenodo.5676893}
+  author       = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Baoqiang Han},
+  title        = {CCMusic: an Open and Diverse Database for Chinese and General Music Information Retrieval Research},
+  month        = {mar},
+  year         = {2024},
+  publisher    = {HuggingFace},
+  version      = {1.2},
+  url          = {https://huggingface.co/ccmusic-database}
 }
 ```
 ### Contributions
-
-Provide a training dataset for acapella scoring system
+Provide a training dataset for the acapella scoring system

acapella.py

@@ -5,34 +5,35 @@ from datasets.tasks import AudioClassification
 
 
 _NAMES = {
-    'songs': ['song' + str(i) for i in range(1, 7)],
-    'singers': ['singer' + str(i) for i in range(1, 23)]
+    "songs": ["song" + str(i) for i in range(1, 7)],
+    "singers": ["singer" + str(i) for i in range(1, 23)],
 }
 
-_HOMEPAGE = f"https://huggingface.co/datasets/ccmusic-database/{os.path.basename(__file__).split('.')[0]}"
+_DBNAME = os.path.basename(__file__).split(".")[0]
+
+_DOMAIN = f"https://www.modelscope.cn/api/v1/datasets/ccmusic/{_DBNAME}/repo?Revision=master&FilePath=data"
+
+_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic/{_DBNAME}"
 
 _CITATION = """\
 @dataset{zhaorui_liu_2021_5676893,
-  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li},
-  title        = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
-  month        = {nov},
-  year         = {2021},
-  publisher    = {Zenodo},
-  version      = {1.1},
-  doi          = {10.5281/zenodo.5676893},
-  url          = {https://doi.org/10.5281/zenodo.5676893}
+  author       = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Zijin Li},
+  title        = {CCMusic: an Open and Diverse Database for Chinese and General Music Information Retrieval Research},
+  month        = {mar},
+  year         = {2024},
+  publisher    = {HuggingFace},
+  version      = {1.2},
+  url          = {https://huggingface.co/ccmusic-database}
 }
 """
 
 _DESCRIPTION = """\
-This database contains 6 Mandarin song segments sung by 22 singers, totaling 132 audio clips. 
-Each segment consists of a verse and a chorus. Four judges evaluate the singing from nine aspects 
-which are pitch, rhythm, vocal range, timbre, pronunciation, vibrato, dynamic, breath control and 
-overall performance on a 10-point scale. The scores are recorded in a sheet.
+This raw dataset 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 have consolidated the raw vocal recordings with their corresponding assessments. The dataset is divided into six segments, each representing a different song, resulting in a total of six divisions. Each segment contains 22 entries, with each entry detailing the vocal recording of an individual singer sampled at 44,100 Hz, the singer's ID, and evaluations across the nine dimensions previously mentioned. Consequently, each entry encompasses 11 columns of data. This dataset is well-suited for tasks such as vocal analysis and regression-based singing voice rating. For instance, as previously stated, the final column of each entry denotes the overall performance score, allowing the audio to be utilized as data and this score to serve as the label for regression analysis.
 """
 
-_URLS = {
-    song: f"{_HOMEPAGE}/resolve/main/data/{song}.zip" for song in _NAMES['songs']}
+_URLS = {"audio": f"{_DOMAIN}/audio.zip", "mel": f"{_DOMAIN}/mel.zip"}
 
 
 class acapella(datasets.GeneratorBasedBuilder):
@@ -40,8 +41,9 @@ class acapella(datasets.GeneratorBasedBuilder):
         return datasets.DatasetInfo(
             features=datasets.Features(
                 {
-                    "song": datasets.Audio(sampling_rate=44_100),
-                    "singer_id": datasets.features.ClassLabel(names=_NAMES['singers']),
+                    "audio": datasets.Audio(sampling_rate=22050),
+                    "mel": datasets.Image(),
+                    "singer_id": datasets.features.ClassLabel(names=_NAMES["singers"]),
                     "pitch": datasets.Value("float64"),
                     "rhythm": datasets.Value("float64"),
                     "vocal_range": datasets.Value("float64"),
@@ -53,7 +55,7 @@ class acapella(datasets.GeneratorBasedBuilder):
                     "overall_performance": datasets.Value("float64"),
                 }
             ),
-            supervised_keys=("song", "singer_id"),
+            supervised_keys=("audio", "singer_id"),
             homepage=_HOMEPAGE,
             license="mit",
             citation=_CITATION,
@@ -61,45 +63,77 @@ class acapella(datasets.GeneratorBasedBuilder):
             task_templates=[
                 AudioClassification(
                     task="audio-classification",
-                    audio_column="song",
+                    audio_column="audio",
                     label_column="singer_id",
                 )
             ],
         )
 
     def _split_generators(self, dl_manager):
-        data_files = dl_manager.download_and_extract(_URLS)
-        split_generator = []
+        songs = {}
+        for index in _NAMES["songs"]:
+            csv_files = dl_manager.download(f"{_DOMAIN}/{index}.csv")
+            song_eval = pd.read_csv(csv_files, index_col="singer_id")
+            scores = []
+            for id in range(22):
+                scores.append(
+                    {
+                        "pitch": song_eval.iloc[id]["pitch"],
+                        "rhythm": song_eval.iloc[id]["rhythm"],
+                        "vocal_range": song_eval.iloc[id]["vocal_range"],
+                        "timbre": song_eval.iloc[id]["timbre"],
+                        "pronunciation": song_eval.iloc[id]["pronunciation"],
+                        "vibrato": song_eval.iloc[id]["vibrato"],
+                        "dynamic": song_eval.iloc[id]["dynamic"],
+                        "breath_control": song_eval.iloc[id]["breath_control"],
+                        "overall_performance": song_eval.iloc[id][
+                            "overall_performance"
+                        ],
+                    }
+                )
 
-        for index in _URLS.keys():
+            songs[index] = scores
+
+        audio_files = dl_manager.download_and_extract(_URLS["audio"])
+        for path in dl_manager.iter_files([audio_files]):
+            fname = os.path.basename(path)
+            if fname.endswith(".wav"):
+                song_id = os.path.basename(os.path.dirname(path))
+                singer_id = int(fname.split("(")[1].split(")")[0]) - 1
+                songs[song_id][singer_id]["audio"] = path
+
+        mel_files = dl_manager.download_and_extract(_URLS["mel"])
+        for path in dl_manager.iter_files([mel_files]):
+            fname = os.path.basename(path)
+            if fname.endswith(".jpg"):
+                song_id = os.path.basename(os.path.dirname(path))
+                singer_id = int(fname.split("(")[1].split(")")[0]) - 1
+                songs[song_id][singer_id]["mel"] = path
+
+        split_generator = []
+        for key in songs.keys():
             split_generator.append(
                 datasets.SplitGenerator(
-                    name=index,
-                    gen_kwargs={
-                        "files": dl_manager.iter_files([data_files[index]]),
-                        "labels": dl_manager.download(f"{_HOMEPAGE}/resolve/main/data/{index}.csv"),
-                    },
+                    name=key,
+                    gen_kwargs={"files": songs[key]},
                 )
             )
 
         return split_generator
 
-    def _generate_examples(self, files, labels):
-        song_eval = pd.read_csv(labels, index_col='singer_id')
+    def _generate_examples(self, files):
         for i, path in enumerate(files):
-            file_name = os.path.basename(path)
-            if file_name.endswith(".wav"):
-                id = int(file_name.split('(')[1].split(')')[0]) - 1
-                yield i, {
-                    "song": path,
-                    "singer_id": id,
-                    "pitch": song_eval.iloc[id]['pitch'],
-                    "rhythm": song_eval.iloc[id]['rhythm'],
-                    "vocal_range": song_eval.iloc[id]['vocal_range'],
-                    "timbre": song_eval.iloc[id]['timbre'],
-                    "pronunciation": song_eval.iloc[id]['pronunciation'],
-                    "vibrato": song_eval.iloc[id]['vibrato'],
-                    "dynamic": song_eval.iloc[id]['dynamic'],
-                    "breath_control": song_eval.iloc[id]['breath_control'],
-                    "overall_performance": song_eval.iloc[id]['overall_performance'],
-                }
+            yield i, {
+                "audio": path["audio"],
+                "mel": path["mel"],
+                "singer_id": i,
+                "pitch": path["pitch"],
+                "rhythm": path["rhythm"],
+                "vocal_range": path["vocal_range"],
+                "timbre": path["timbre"],
+                "pronunciation": path["pronunciation"],
+                "vibrato": path["vibrato"],
+                "dynamic": path["dynamic"],
+                "breath_control": path["breath_control"],
+                "overall_performance": path["overall_performance"],
+            }

data/{song1.zip → song1 (16).wav}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:654459126fc308a456d75911713ca3e2ca1b7ab37433fd00b5c78b0f5f7df8c2
-size 216567277
+oid sha256:10a37c78d5eaa49c0e637338195ec96ac63ba1cf155b8835908cf568ca8b76df
+size 12642086

data/song1.csv

@@ -1,23 +0,0 @@
-singer_id,pitch,rhythm,vocal_range,timbre,pronunciation,vibrato,dynamic,breath_control,overall_performance
-0,2,3.5,2.75,1.75,2.25,1.25,3,3.25,2.25
-1,5,5.5,3.75,4.25,2,1.5,3.5,4,5
-2,4.75,4.25,3,2.75,3.25,2.25,2.75,3.25,4.5
-3,6.75,6.5,6,6.5,6,5,5.25,4.5,6
-4,4,4.25,4.25,3,2,2,2.25,2.5,5.5
-5,4.75,4.25,5,5.5,4.5,3.75,4.25,4.5,4.75
-6,5.5,5.5,5,4.5,4.5,3.5,3.5,4.25,5.25
-7,9,9.75,9.25,9.75,9.5,8.5,9.5,10,10
-8,5.25,5,4.75,5,3,1.75,2.25,2.25,3.75
-9,5,3.5,2.75,2.75,2,1.75,1.75,2,3.25
-10,2.75,3,2.25,2.5,2,2,2.5,2.5,3
-11,5.75,5,4.75,5,4.75,3,3,3,5
-12,3.5,4,3.75,3.75,3.25,3.75,4,3.5,4
-13,9.75,9.75,9.75,9,10,9.5,9.5,9.5,10
-14,1.75,1.75,1.5,1.25,1.75,1.75,1.25,1,1.75
-15,9.5,9,9.25,9.75,9.75,9.5,9.25,9.5,9.5
-16,6.25,5,4.25,5,4.75,2.75,3,3.75,3.75
-17,5.25,5,5.25,4.25,4.25,3.25,3.75,3.5,5.25
-18,3.25,3,2.5,1.5,2,1.75,2.25,2.25,1.5
-19,5,4.75,4.5,4.5,3.75,3,2.5,3,4.25
-20,9.5,9.5,9.5,8.5,9.5,10,9.5,9.75,9.75
-21,6.5,6.25,5.5,5.5,4.25,3,4.25,4.5,5.5

data/song2.csv

@@ -1,23 +0,0 @@
-singer_id,pitch,rhythm,vocal_range,timbre,pronunciation,vibrato,dynamic,breath_control,overall_performance
-0,1.5,2,1.5,1.75,2,1.5,1.25,1.75,1.5
-1,4,4,4.5,3.25,2.5,1.75,2.25,1.5,3.25
-2,4.75,3.75,2.75,2.75,2.25,2.25,2.25,2.25,3.25
-3,5.5,5,5.25,5,5.25,5.5,4.75,4.75,5.25
-4,4.5,4.25,3.5,3.25,3.75,4,2.5,3.25,4
-5,5,4.25,4.5,5,4.5,3.5,4.75,3.75,4.25
-6,5.5,5,4.75,4.75,4.75,4,2.75,3.5,4.5
-7,10,10,10,10,9.75,9.5,9.75,9.75,10
-8,5,4,4.5,4,4.75,3.25,4.25,3.75,5
-9,4.5,3.5,3,3.25,2.5,2.5,2.5,2.75,4
-10,2,2,1.75,1.25,1.5,2.25,2,1.75,2
-11,5.25,4.75,4.25,3.75,3,2.75,3.25,3.25,4.75
-12,3.75,4.5,2.75,3.75,3.75,3.75,3,4.75,4.5
-13,9.75,9.75,9.5,9.25,10,10,10,9.5,10
-14,1.25,1,1,1.25,1.25,1,1,1,1
-15,10,9.75,9.75,9.5,9.5,10,10,10,9.5
-16,5,5.5,6,5.5,4.5,2.75,3,4.25,5
-17,5,5.25,5.75,5.5,4.75,4.25,3.5,3.5,5.25
-18,1.75,2.5,1.5,1.5,2.5,2.5,2,2,2.75
-19,3.75,3.75,3.75,4,4,3.75,3,3.25,3.5
-20,9.75,9.5,9.25,9.75,10,9.25,9.75,9.5,9.5
-21,5.5,5,6.25,5.75,5,5,5.5,4.5,5.5

data/song2.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:11190cbcd30a2e2e071125c1fe088aec7402e04bc7335befaf53c4bad1bb5da2
-size 193964450

data/song3.csv

@@ -1,23 +0,0 @@
-singer_id,pitch,rhythm,vocal_range,timbre,pronunciation,vibrato,dynamic,breath_control,overall_performance
-0,1.75,3,2,1.75,1.5,1,1.5,2.25,3
-1,4,4,4.25,3.75,2.5,2.25,1.75,1.75,2.5
-2,4.75,4,3,2.75,2.25,2,1.75,1.5,3
-3,5.5,5.5,5.5,5,5.75,5.25,3.5,3.5,4.25
-4,4.25,4,4,2.5,2.25,3,2.5,3,3.75
-5,5,4.5,4.25,4.75,4.5,3,5,3.75,4.25
-6,5.5,5.25,4.75,4.5,4.5,3.75,2.75,2.25,3.75
-7,9.75,10,9.75,9.75,10,9.5,9.75,9.75,9.5
-8,5,4.75,4.75,4.75,4.25,3.5,4,3.25,4.25
-9,4,3.5,3,3.5,2.25,2,1.5,2,3.75
-10,1.75,2.25,2.5,1.75,2.5,2.5,2,1.5,2.5
-11,5.75,5.25,5,4.5,2.75,3,4,3.25,4.75
-12,4.5,3.5,3,3.75,3.75,3,3.5,2.75,3.5
-13,9.75,10,10,9.5,10,10,9,10,9.75
-14,1.5,1,1,1,1,1,1,1,1.25
-15,10,10,10,10,9.5,10,9.5,10,10
-16,6.25,5,4.75,5,3.5,3.5,3.25,3.5,4.75
-17,5.25,4.25,4.25,5,4.5,3.5,3.75,3,4.75
-18,2.5,2.25,1.75,1.5,1.5,1.25,1.75,2,2.5
-19,4,3.5,3.5,4,4,3.25,2.75,2,3.25
-20,10,9.75,9.5,10,9.75,9.75,9.5,10,9.5
-21,6,5.75,5.5,5.5,3.5,4.5,5.25,4,5.25

data/song4.csv

@@ -1,23 +0,0 @@
-singer_id,pitch,rhythm,vocal_range,timbre,pronunciation,vibrato,dynamic,breath_control,overall_performance
-0,1.5,2.75,2.5,2,1.5,1,1.5,2.25,2.25
-1,4.75,5,5,6,5,3.25,3.75,3.5,5
-2,4.25,3.25,3,2.25,2.5,3,1.75,1.25,3.5
-3,5.5,6.25,4.75,4.25,5.75,5,4.5,4.5,5.75
-4,4.25,4,3.25,3,1.75,1.75,2.25,1.75,4
-5,4.25,4.75,4.75,4.5,3.75,4.25,4,3.75,4.75
-6,6.25,5,4,4.25,4,2.25,2.5,2.75,5.25
-7,10,9.75,10,10,9.5,9,10,10,9.75
-8,5.5,4.5,4.25,4,3.5,1.5,3.5,3.5,4.5
-9,4.25,3.5,3,2.25,1.5,1.5,1.75,2,2.75
-10,2.25,2.25,1.5,1.5,2,2.25,2.25,1.75,2.5
-11,6,6,5,4.25,4,3,3.25,3,4.75
-12,3.5,4.25,3.5,4.25,4.5,3,2.75,3.25,4
-13,9.5,10,10,9.5,9.75,9.75,9.75,9.75,9.75
-14,1.75,1.25,1.75,1.25,1.5,1.25,1.25,1,1.75
-15,9.25,10,9.5,9.75,9.25,9.25,9.5,9.75,9.75
-16,6,5.75,4.25,5,6,4.25,3.25,5.75,6
-17,5.75,5.5,5.25,5.25,4,3.5,4.25,4,5.5
-18,2,2.25,2.5,2.5,2,2.5,2,1.75,1.5
-19,3.75,4.25,3.75,4,2.25,3.5,2.25,2,3.75
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-21,5.75,6.25,6.25,5.75,4,4,4,4.25,6

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