sync ms

README.md

@@ -15,32 +15,54 @@ viewer: false
 ---
 
 # Dataset Card for Piano Sound Quality Dataset
+The raw dataset comprises 12 full-range audio files in .wav/.mp3/.m4a format representing seven models of pianos: Kawai upright piano, Kawai grand piano, Young Change upright piano, Hsinghai upright piano, Grand Theatre Steinway piano, Steinway grand piano, and Pearl River upright piano. Additionally, there are 1,320 split monophonic audio files in .wav/.mp3/.m4a format, bringing the total number of files to 1,332. Furthermore, the dataset includes a score sheet in .xls format containing subjective evaluations of piano sound quality provided by 29 participants with musical backgrounds.
+
 ## Usage
+### Raw Subset
 ```python
 from datasets import load_dataset
 
-data = load_dataset("ccmusic-database/pianos", split="train")
-labels = data.features['label'].names
+ds = load_dataset("ccmusic-database/pianos", name="default")
+for item in ds["train"]:
+    print(item)
+
+for item in ds["validation"]:
+    print(item)
 
-for item in data:
-    print('piano spec: ', item['mel'])
-    print('label name: ' + labels[item['label']])
+for item in ds["test"]:
+    print(item)
+```
+
+### Eval Subset
+```python
+from datasets import load_dataset
+
+ds = load_dataset("ccmusic-database/pianos", name="eval")
+for item in ds["train"]:
+    print(item)
+
+for item in ds["validation"]:
+    print(item)
+
+for item in ds["test"]:
+    print(item)
 ```
 
 ## Maintenance
 ```bash
 GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/pianos
+cd pianos
 ```
 
 ## Dataset Description
 - **Homepage:** <https://ccmusic-database.github.io>
 - **Repository:** <https://huggingface.co/datasets/CCMUSIC/pianos>
 - **Paper:** <https://doi.org/10.5281/zenodo.5676893>
-- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
+- **Leaderboard:** <https://www.modelscope.cn/datasets/ccmusic/pianos>
 - **Point of Contact:** <https://arxiv.org/abs/2310.04722>
 
 ### Dataset Summary
-This dataset contains 12 gamut audio files (.wav / .mp3 / .m4a format) and 1320 split single-tone audio files (.wav / .mp3 / .m4a format) of 7 types of pianos (Kawai upright piano, Kawai grand piano, YOUNG CHANG upright piano, HSINGHAI upright piano, Steinway grand piano in grand theatre, Steinway grand piano and Pearl River upright piano) in the piano-room of China Conservatory of Music, a total of 1332 files. In addition, there is a questionnaire on subjective evaluation of piano sound quality (.xls format), including the score of 29 people participating in the subjective evaluation of piano sound quality.
+Due to the need to increase the dataset size and the absence of a popular piano brand, Yamaha, the dataset is expanded by recording an upright Yamaha piano in [[1]](https://arxiv.org/pdf/2310.04722.pdf), in which the recording details can be found. This results in a total of 2,020 audio files. As models used in that article require a larger dataset, data augmentation was performed. The original audio was transformed into Mel spectrograms and sliced into 0.18-second segments, a parameter chosen based on empirical experience. This results in 18,745 spectrogram slices. Although 0.18 seconds may seem narrow, this duration is sufficient for the task at hand, as the classification of piano sound quality does not heavily rely on the temporal characteristics of the audio segments.
 
 ### Supported Tasks and Leaderboards
 Piano Sound Classification, pitch detection
@@ -49,23 +71,45 @@ Piano Sound Classification, pitch detection
 English
 
 ## Dataset Structure
+### Eval Subset
 <style>
-  #pianos td {
+  .pianos td {
     vertical-align: middle !important;
     text-align: center;
   }
-  #pianos th {
+  .pianos th {
     text-align: center;
   }
 </style>
-<table id="pianos">
+<table class="pianos">
     <tr>
         <th>mel(.jpg, 0.18s)</th>
         <th>label(8-class)</th>
         <th>pitch(88-class)</th>
     </tr>
     <tr>
-        <td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/TYYnuJqndeWzXLJMmOyXJ.jpeg"></td>
+        <td><img src="./data/TYYnuJqndeWzXLJMmOyXJ.jpeg"></td>
+        <td>PearlRiver / YoungChang / Steinway-T / Hsinghai / Kawai / Steinway / Kawai-G / Yamaha</td>
+        <td>88 pitches on piano</td>
+    </tr>
+    <tr>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+    </tr>
+</table>
+
+### Raw Subset
+<table class="pianos">
+    <tr>
+        <th>audio(.wav, 22050Hz)</th>
+        <th>mel(.jpg)</th>
+        <th>label(8-class)</th>
+        <th>pitch(88-class)</th>
+    </tr>
+    <tr>
+        <td><audio controls src="./data/3800.wav"></audio></td>
+        <td><img src="./data/3800.jpg"></td>
         <td>PearlRiver / YoungChang / Steinway-T / Hsinghai / Kawai / Steinway / Kawai-G / Yamaha</td>
         <td>88 pitches on piano</td>
     </tr>
@@ -73,6 +117,7 @@ English
         <td>...</td>
         <td>...</td>
         <td>...</td>
+        <td>...</td>
     </tr>
 </table>
 
@@ -91,12 +136,13 @@ English
 8_Yamaha
 ```
 
-### Data Splits 
-|      total      | 18745 |
-| :-------------: | :---: |
-|   train(80%)    | 14996 |
-| validation(10%) | 1874  |
-|    test(10%)    | 1875  |
+### Data Splits for Eval Subset
+|      Split      | Eval  | Eval  |
+| :-------------: | :---: | :---: |
+|      total      | 18745 |  668  |
+|   train(80%)    | 14996 |  534  |
+| validation(10%) | 1874  |  67   |
+|    test(10%)    | 1875  |  67   |
 
 ## Dataset Creation
 ### Curation Rationale
@@ -120,7 +166,7 @@ Piano brands
 
 ## Considerations for Using the Data
 ### Social Impact of Dataset
-Help developing piano sound quality scoring apps
+Help develop piano sound quality scoring apps
 
 ### Discussion of Biases
 Only for pianos
@@ -133,9 +179,9 @@ Lack of black keys for Steinway, data imbalance
 Zijin Li
 
 ### Evaluation
-[Monan Zhou, Shangda Wu, Shaohua Ji, Zijin Li, and Wei Li. A Holistic Evaluation of Piano Sound Quality[C]//Proceedings of the 10th Conference on Sound and Music Technology (CSMT). Springer, Singapore, 2023.](https://arxiv.org/pdf/2310.04722.pdf)
+[1] [Monan Zhou, Shangda Wu, Shaohua Ji, Zijin Li, and Wei Li. A Holistic Evaluation of Piano Sound Quality[C]//Proceedings of the 10th Conference on Sound and Music Technology (CSMT). Springer, Singapore, 2023.](https://arxiv.org/pdf/2310.04722.pdf)
 
-(Note: this paper only uses the first 7 piano classes in the dataset.)
+(Note: this paper only uses the first 7 piano classes in the dataset, its future work has finished the 8-class evaluation)
 
 ### Licensing Information
 ```
@@ -163,16 +209,15 @@ 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}
 }
 ```
 

data/{pianos_rawdata.zip → 3800.wav}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:962f4f5e1c94e721155043f86234f5e133ac0cd16b4e98eb53f2112c6d1b2fc1
-size 352375886
+oid sha256:a875d995b0372d4a0a33a67a2b53032667898660162c30f23cf0655406034710
+size 333604

pianos.py

@@ -1,8 +1,7 @@
 import os
 import random
-import socket
 import datasets
-from datasets.tasks import ImageClassification
+from datasets.tasks import ImageClassification, AudioClassification
 
 
 _NAMES = [
@@ -16,20 +15,21 @@ _NAMES = [
     "Yamaha",
 ]
 
-_NAME = os.path.basename(__file__).split(".")[0]
+_DBNAME = os.path.basename(__file__).split(".")[0]
 
-_HOMEPAGE = f"https://huggingface.co/datasets/ccmusic-database/{_NAME}"
+_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic/{_DBNAME}"
+
+_DOMAIN = f"https://www.modelscope.cn/api/v1/datasets/ccmusic/{_DBNAME}/repo?Revision=master&FilePath=data"
 
 _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}
 }
 """
 
@@ -128,11 +128,26 @@ _PITCHES = {
     "800": "c5",
 }
 
+_URLS = {
+    "audio": f"{_DOMAIN}/audio.zip",
+    "mel": f"{_DOMAIN}/mel.zip",
+    "eval": f"{_DOMAIN}/eval.zip",
+}
+
+
+class pianos_Config(datasets.BuilderConfig):
+    def __init__(self, features, supervised_keys, task_templates, **kwargs):
+        super(pianos_Config, self).__init__(version=datasets.Version("0.1.2"), **kwargs)
+        self.features = features
+        self.supervised_keys = supervised_keys
+        self.task_templates = task_templates
+
 
 class pianos(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
+    VERSION = datasets.Version("0.1.2")
+    BUILDER_CONFIGS = [
+        pianos_Config(
+            name="eval",
             features=datasets.Features(
                 {
                     "mel": datasets.Image(),
@@ -143,9 +158,6 @@ class pianos(datasets.GeneratorBasedBuilder):
                 }
             ),
             supervised_keys=("mel", "label"),
-            homepage=_HOMEPAGE,
-            license="mit",
-            citation=_CITATION,
             task_templates=[
                 ImageClassification(
                     task="image-classification",
@@ -153,31 +165,74 @@ class pianos(datasets.GeneratorBasedBuilder):
                     label_column="label",
                 )
             ],
-        )
-
-    def _cdn_url(self, ip="127.0.0.1", port=80):
-        try:
-            # easy for local test
-            with socket.create_connection((ip, port), timeout=5):
-                return f"http://{ip}/hf/{_NAME}/data/pianos_data.zip"
+        ),
+        pianos_Config(
+            name="default",
+            features=datasets.Features(
+                {
+                    "audio": datasets.Audio(sampling_rate=22050),
+                    "mel": datasets.Image(),
+                    "label": datasets.features.ClassLabel(names=_NAMES),
+                    "pitch": datasets.features.ClassLabel(
+                        names=list(_PITCHES.values())
+                    ),
+                }
+            ),
+            supervised_keys=("audio", "label"),
+            task_templates=[
+                AudioClassification(
+                    task="audio-classification",
+                    audio_column="audio",
+                    label_column="label",
+                )
+            ],
+        ),
+    ]
 
-        except (socket.timeout, socket.error):
-            return f"{_HOMEPAGE}/resolve/main/data/pianos_data.zip"
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=self.config.features,
+            homepage=_HOMEPAGE,
+            license="mit",
+            citation=_CITATION,
+            supervised_keys=self.config.supervised_keys,
+            task_templates=self.config.task_templates,
+        )
 
     def _split_generators(self, dl_manager):
-        data_files = dl_manager.download_and_extract(self._cdn_url())
         dataset = []
-
-        for path in dl_manager.iter_files([data_files]):
-            fname = os.path.basename(path)
-            if fname.endswith(".jpg"):
-                dataset.append(
-                    {
-                        "mel": path,
+        if self.config.name == "eval":
+            data_files = dl_manager.download_and_extract(_URLS["eval"])
+            for path in dl_manager.iter_files([data_files]):
+                fname = os.path.basename(path)
+                if fname.endswith(".jpg"):
+                    dataset.append(
+                        {
+                            "mel": path,
+                            "label": os.path.basename(os.path.dirname(path)),
+                            "pitch": _PITCHES[fname.split("_")[0]],
+                        }
+                    )
+        else:
+            subset = {}
+            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"):
+                    subset[fname.split(".")[0]] = {
+                        "audio": path,
                         "label": os.path.basename(os.path.dirname(path)),
-                        "pitch": _PITCHES[fname.split("_")[0]],
+                        "pitch": _PITCHES[fname[1:4]],
                     }
-                )
+
+            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"):
+                    subset[fname.split(".")[0]]["mel"] = path
+
+            dataset = list(subset.values())
 
         random.shuffle(dataset)
         count = len(dataset)
@@ -197,9 +252,19 @@ class pianos(datasets.GeneratorBasedBuilder):
         ]
 
     def _generate_examples(self, files):
-        for i, path in enumerate(files):
-            yield i, {
-                "mel": path["mel"],
-                "label": path["label"],
-                "pitch": path["pitch"],
-            }
+        if self.config.name == "eval":
+            for i, path in enumerate(files):
+                yield i, {
+                    "mel": path["mel"],
+                    "label": path["label"],
+                    "pitch": path["pitch"],
+                }
+
+        else:
+            for i, path in enumerate(files):
+                yield i, {
+                    "audio": path["audio"],
+                    "mel": path["mel"],
+                    "label": path["label"],
+                    "pitch": path["pitch"],
+                }