upl base codes

.gitignore

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+rename.sh
+test.py

README.md

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 ---
 license: mit
+task_categories:
+- mir
+language:
+- en
+tags:
+- music
+- art
+pretty_name: Song Structure Annotation Database
+size_categories:
+- n<1K
 ---
+
+## Dataset Description
+- **Homepage:** <https://ccmusic-database.github.io>
+- **Repository:** <https://huggingface.co/datasets/CCMUSIC/song_structure>
+- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
+- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
+- **Point of Contact:** N/A
+
+### Dataset Summary
+This database contains 300 pop songs (.mp3 format, downloaded from NetEase Cloud Music), as well as a structure annotation file (.txt format) for each song. The song structure is labeled as follows: intro, chorus, verse, pre-chorus, post-chorus, bridge, ending.
+
+### Supported Tasks and Leaderboards
+mir
+
+### Languages
+Chinese, English
+
+## Dataset Structure
+### Data Instances
+.wav, .txt
+
+### Data Fields
+```
+intro, chorus, verse, pre-chorus, post-chorus, bridge, ending
+```
+
+### Data Splits
+train, valid, test
+
+## Dataset Creation
+### Curation Rationale
+Lack of a dataset for Song Structure
+
+### Source Data
+#### Initial Data Collection and Normalization
+Zhaorui Liu, Monan Zhou
+
+#### Who are the source language producers?
+Students from CCMUSIC
+
+### Annotations
+#### Annotation process
+Students from CCMUSIC collected 300 pop songs, as well as a structure annotation file for each song
+
+#### Who are the annotators?
+Students from CCMUSIC
+
+### Personal and Sensitive Information
+Due to copyright issues with the original music, only features of audios by frame are provided in the dataset
+
+## Considerations for Using the Data
+### Social Impact of Dataset
+Promoting the development of AI music industry
+
+### Discussion of Biases
+Only for mp3
+
+### Other Known Limitations
+Most are Chinese songs
+
+## Additional Information
+### Dataset Curators
+Zijin Li
+
+### Licensing Information
+```
+MIT License
+
+Copyright (c) 2023 CCMUSIC
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
+
+### Citation Information
+```
+@dataset{zhaorui_liu_2021_5676893,
+  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu and Zijin Li},
+  title        = {{Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)}},
+  month        = nov,
+  year         = 2021,
+  publisher    = {Zenodo},
+  version      = {1.1},
+  doi          = {10.5281/zenodo.5676893},
+  url          = {https://doi.org/10.5281/zenodo.5676893}
+}
+```
+
+### Contributions
+Provide a dataset for Song Structure

data/labels.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:adb8198ac4099b50709ee0f36567a8213ce6e265a8a0b02a2fadf91e6ed165da
+size 111232

song_structure.py

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+import os
+import datasets
+from datasets.tasks import AudioClassification
+
+
+# Once upload a new piano brand, please register its name here
+_NAMES = ["intro", "chorus", "verse", "pre-chorus", "post-chorus", "bridge"]
+
+_DBNAME = os.path.basename(__file__).split('.')[0]
+
+_HOMEPAGE = "https://huggingface.co/datasets/ccmusic-database/" + _DBNAME
+
+_CITATION = """\
+@dataset{zhaorui_liu_2021_5676893,
+  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu and Zijin Li},
+  title        = {{Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)}},
+  month        = nov,
+  year         = 2021,
+  publisher    = {Zenodo},
+  version      = {1.1},
+  doi          = {10.5281/zenodo.5676893},
+  url          = {https://doi.org/10.5281/zenodo.5676893}
+}
+"""
+
+_DESCRIPTION = """\
+This database contains 300 pop songs (.mp3 format, downloaded from NetEase Cloud Music), 
+as well as a structure annotation file (.txt format) for each song. 
+The song structure is labeled as follows: 
+intro, chorus, verse, pre-chorus, post-chorus, bridge, ending.
+"""
+
+_URL = _HOMEPAGE + "/resolve/main/data/labels.zip"
+
+
+class piano_sound_quality(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "time": datasets.Value('time32'),
+                    "audio": datasets.Value('binary'),
+                    "label": datasets.features.ClassLabel(names=_NAMES),
+                }
+            ),
+            supervised_keys=("time", "label"),
+            homepage=_HOMEPAGE,
+            license="mit",
+            citation=_CITATION,
+            task_templates=[
+                AudioClassification(
+                    task="audio-classification",
+                    audio_column="time",
+                    label_column="label",
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files]),
+                },
+            )
+        ]
+
+    def _generate_examples(self, files):
+        for i, path in enumerate(files):
+            file_name = os.path.basename(path)
+            if file_name.endswith(".wav"):
+                yield i, {
+                    "time": 0,
+                    "audio": path,
+                    "label": 0,
+                }