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+rename.sh

README.md

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 ---
-license: mit
+license: cc-by-nc-nd-4.0
+task_categories:
+- audio-classification
+- image-classification
+language:
+- zh
+- en
+tags:
+- music
+- art
+pretty_name: Music Genre Dataset
+size_categories:
+- 10K<n<100K
+viewer: false
 ---
+
+# Dataset Card for Music Genre
+The Default dataset comprises approximately 1,700 musical pieces in .mp3 format, sourced from the NetEase music. The lengths of these pieces range from 270 to 300 seconds. All are sampled at the rate of 22,050 Hz. As the website providing the audio music includes style labels for the downloaded music, there are no specific annotators involved. Validation is achieved concurrently with the downloading process. They are categorized into a total of 16 genres.
+
+## Viewer
+<https://www.modelscope.cn/datasets/ccmusic-database/music_genre/dataPeview>
+
+## Dataset Structure
+<style>
+  .genres td {
+    vertical-align: middle !important;
+    text-align: center;
+  }
+  .genres th {
+    text-align: center;
+  }
+</style>
+
+### Default Subset
+<table class="genres">
+    <tr>
+        <th>audio</th>
+        <th>mel (spectrogram)</th>
+        <th>fst_level_label (2-class)</th>
+        <th>sec_level_label (9-class)</th>
+        <th>thr_level_label (16-class)</th>
+    </tr>
+    <tr>
+        <td>.wav, 22050Hz</td>
+        <td>.jpg, 22050Hz</td>
+        <td>1_Classic / 2_Non_classic</td>
+        <td>3_Symphony / 4_Opera / 5_Solo / 6_Chamber / 7_Pop / 8_Dance_and_house / 9_Indie / 10_Soul_or_r_and_b / 11_Rock</td>
+        <td>3_Symphony / 4_Opera / 5_Solo / 6_Chamber / 12_Pop_vocal_ballad / 13_Adult_contemporary / 14_Teen_pop / 15_Contemporary_dance_pop / 16_Dance_pop / 17_Classic_indie_pop / 18_Chamber_cabaret_and_art_pop / 10_Soul_or_r_and_b / 19_Adult_alternative_rock / 20_Uplifting_anthemic_rock / 21_Soft_rock / 22_Acoustic_pop</td>
+    </tr>
+    <tr>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+    </tr>
+</table>
+
+### Eval Subset
+<table class="genres">
+    <tr>
+        <th>mel</th>
+        <th>cqt</th>
+        <th>chroma</th>
+        <th>fst_level_label (2-class)</th>
+        <th>sec_level_label (9-class)</th>
+        <th>thr_level_label (16-class)</th>
+    </tr>
+    <tr>
+        <td>.jpg, 11.4s, 48000Hz</td>
+        <td>.jpg, 11.4s, 48000Hz</td>
+        <td>.jpg, 11.4s, 48000Hz</td>
+        <td>1_Classic / 2_Non_classic</td>
+        <td>3_Symphony / 4_Opera / 5_Solo / 6_Chamber / 7_Pop / 8_Dance_and_house / 9_Indie / 10_Soul_or_r_and_b / 11_Rock</td>
+        <td>3_Symphony / 4_Opera / 5_Solo / 6_Chamber / 12_Pop_vocal_ballad / 13_Adult_contemporary / 14_Teen_pop / 15_Contemporary_dance_pop / 16_Dance_pop / 17_Classic_indie_pop / 18_Chamber_cabaret_and_art_pop / 10_Soul_or_r_and_b / 19_Adult_alternative_rock / 20_Uplifting_anthemic_rock / 21_Soft_rock / 22_Acoustic_pop</td>
+    </tr>
+    <tr>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+    </tr>
+</table>
+
+### Data Instances
+.zip(.jpg)
+<img src="./data/labelv.png">
+
+### Data Fields
+```
+1_Classic
+    3_Symphony
+    4_Opera
+    5_Solo
+    6_Chamber
+
+2_Non_classic
+    7_Pop
+        12_Pop_vocal_ballad
+        13_Adult_contemporary
+        14_Teen_pop
+
+    8_Dance_and_house
+        15_Contemporary_dance_pop
+        16_Dance_pop
+
+    9_Indie
+        17_Classic_indie_pop
+        18_Chamber_cabaret_and_art_pop
+
+    10_Soul_or_RnB
+
+    11_Rock
+        19_Adult_alternative_rock
+        20_Uplifting_anthemic_rock
+        21_Soft_rock
+        22_Acoustic_pop
+```
+<img src="https://www.modelscope.cn/api/v1/datasets/ccmusic-database/music_genre/repo?Revision=master&FilePath=.%2Fdata%2Fgenre.png&View=true">
+
+### Data Splits
+|      Split      | Default | Eval  |
+| :-------------: | :-----: | :---: |
+|      total      |  1713   | 36375 |
+|   train(80%)    |  1370   | 29100 |
+| validation(10%) |   171   | 3637  |
+|    test(10%)    |   172   | 3638  |
+
+## Dataset Description
+- **Homepage:** <https://ccmusic-database.github.io>
+- **Repository:** <https://huggingface.co/datasets/ccmusic-database/music_genre>
+- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
+- **Leaderboard:** <https://www.modelscope.cn/datasets/ccmusic-database/music_genre>
+- **Point of Contact:** <https://huggingface.co/ccmusic-database/music_genre>
+
+### Dataset Summary
+This database contains about 1700 musical pieces (.mp3 format) with lengths of 270-300s that are divided into 17 genres in total.
+
+### Supported Tasks and Leaderboards
+Audio classification
+
+### Languages
+Multilingual
+
+## Maintenance
+```bash
+GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/music_genre
+cd music_genre
+```
+
+## Usage
+### Default Subset
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ccmusic-database/music_genre", 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
+
+dataset = load_dataset("ccmusic-database/music_genre", name="eval")
+for item in ds["train"]:
+    print(item)
+
+for item in ds["validation"]:
+    print(item)
+
+for item in ds["test"]:
+    print(item)
+```
+
+## Dataset Creation
+### Curation Rationale
+Promoting the development of AI in the music industry
+
+### Source Data
+#### Initial Data Collection and Normalization
+Zhaorui Liu, Monan Zhou
+
+#### Who are the source language producers?
+Composers of the songs in the dataset
+
+### Annotations
+#### Annotation process
+Students collected about 1700 musical pieces (.mp3 format) with lengths of 270-300s divided into 17 genres in total.
+
+#### Who are the annotators?
+Students from CCMUSIC
+
+### Personal and Sensitive Information
+Due to copyright issues with the original music, only spectrograms are provided in the dataset.
+
+## Considerations for Using the Data
+### Social Impact of Dataset
+Promoting the development of AI in the music industry
+
+### Discussion of Biases
+Most are English songs
+
+### Other Known Limitations
+Samples are not balanced enough
+
+## Additional Information
+### Dataset Curators
+Zijin Li
+
+### Evaluation
+<https://huggingface.co/ccmusic-database/music_genre>
+
+### Citation Information
+```bibtex
+@dataset{zhaorui_liu_2021_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 dataset for music genre classification

music_genre.py

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+import os
+import random
+import datasets
+from datasets.tasks import ImageClassification
+
+_NAMES_1 = {
+    1: "Classic",
+    2: "Non_classic",
+}
+
+_NAMES_2 = {
+    3: "Symphony",
+    4: "Opera",
+    5: "Solo",
+    6: "Chamber",
+    7: "Pop",
+    8: "Dance_and_house",
+    9: "Indie",
+    10: "Soul_or_RnB",
+    11: "Rock",
+}
+
+_NAMES_3 = {
+    3: "Symphony",
+    4: "Opera",
+    5: "Solo",
+    6: "Chamber",
+    12: "Pop_vocal_ballad",
+    13: "Adult_contemporary",
+    14: "Teen_pop",
+    15: "Contemporary_dance_pop",
+    16: "Dance_pop",
+    17: "Classic_indie_pop",
+    18: "Chamber_cabaret_and_art_pop",
+    10: "Soul_or_RnB",
+    19: "Adult_alternative_rock",
+    20: "Uplifting_anthemic_rock",
+    21: "Soft_rock",
+    22: "Acoustic_pop",
+}
+
+_DBNAME = os.path.basename(__file__).split(".")[0]
+
+_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{_DBNAME}"
+
+_DOMAIN = f"https://www.modelscope.cn/api/v1/datasets/ccmusic-database/{_DBNAME}/repo?Revision=master&FilePath=data"
+
+_CITATION = """\
+@dataset{zhaorui_liu_2021_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}
+}
+"""
+
+_DESCRIPTION = """\
+The raw dataset comprises approximately 1,700 musical pieces in .mp3 format, sourced from the NetEase music. The lengths of these pieces range from 270 to 300 seconds. All are sampled at the rate of 48,000 Hz. As the website providing the audio music includes style labels for the downloaded music, there are no specific annotators involved. Validation is achieved concurrently with the downloading process. They are categorized into a total of 16 genres.
+
+For the pre-processed version, audio is cut into an 11.4-second segment, resulting in 36,375 files, which are then transformed into Mel, CQT and Chroma. In the end, the data entry has six columns: the first three columns represent the Mel, CQT, and Chroma spectrogram slices in .jpg format, respectively, while the last three columns contain the labels for the three levels. The first level comprises two categories, the second level consists of nine categories, and the third level encompasses 16 categories. The entire dataset is shuffled and split into training, validation, and test sets in a ratio of 8:1:1. This dataset can be used for genre classification.
+"""
+
+_URLS = {
+    "audio": f"{_DOMAIN}/audio.zip",
+    "mel": f"{_DOMAIN}/mel.zip",
+    "eval": f"{_DOMAIN}/eval.zip",
+}
+
+
+class music_genre(datasets.GeneratorBasedBuilder):
+    # BUILDER_CONFIGS = [
+    #     datasets.BuilderConfig(name="default"),
+    #     datasets.BuilderConfig(name="eval"),
+    # ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=(
+                datasets.Features(
+                    {
+                        "audio": datasets.Audio(sampling_rate=22050),
+                        "mel": datasets.Image(),
+                        "fst_level_label": datasets.features.ClassLabel(
+                            names=list(_NAMES_1.values())
+                        ),
+                        "sec_level_label": datasets.features.ClassLabel(
+                            names=list(_NAMES_2.values())
+                        ),
+                        "thr_level_label": datasets.features.ClassLabel(
+                            names=list(_NAMES_3.values())
+                        ),
+                    }
+                )
+                if self.config.name == "raw"
+                else datasets.Features(
+                    {
+                        "mel": datasets.Image(),
+                        "cqt": datasets.Image(),
+                        "chroma": datasets.Image(),
+                        "fst_level_label": datasets.features.ClassLabel(
+                            names=list(_NAMES_1.values())
+                        ),
+                        "sec_level_label": datasets.features.ClassLabel(
+                            names=list(_NAMES_2.values())
+                        ),
+                        "thr_level_label": datasets.features.ClassLabel(
+                            names=list(_NAMES_3.values())
+                        ),
+                    }
+                )
+            ),
+            supervised_keys=("mel", "sec_level_label"),
+            homepage=_HOMEPAGE,
+            license="CC-BY-NC-ND",
+            version="1.2.0",
+            citation=_CITATION,
+            description=_DESCRIPTION,
+            task_templates=[
+                ImageClassification(
+                    task="image-classification",
+                    image_column="mel",
+                    label_column="sec_level_label",
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        dataset = []
+        if self.config.name == "raw":
+            files = {}
+            audio_files = dl_manager.download_and_extract(_URLS["audio"])
+            mel_files = dl_manager.download_and_extract(_URLS["mel"])
+            for path in dl_manager.iter_files([audio_files]):
+                fname: str = os.path.basename(path)
+                if fname.endswith(".mp3"):
+                    files[fname.split(".mp")[0]] = {"audio": path}
+
+            for path in dl_manager.iter_files([mel_files]):
+                fname = os.path.basename(path)
+                if fname.endswith(".jpg"):
+                    files[fname.split(".jp")[0]]["mel"] = path
+
+            dataset = list(files.values())
+
+        else:
+            data_files = dl_manager.download_and_extract(_URLS["eval"])
+            for path in dl_manager.iter_files([data_files]):
+                if os.path.basename(path).endswith(".jpg") and "mel" in path:
+                    dataset.append(
+                        {
+                            "mel": path,
+                            "cqt": path.replace("\\mel\\", "\\cqt\\").replace(
+                                "/mel/", "/cqt/"
+                            ),
+                            "chroma": path.replace("\\mel\\", "\\chroma\\").replace(
+                                "/mel/", "/chroma/"
+                            ),
+                        }
+                    )
+
+        random.shuffle(dataset)
+        data_count = len(dataset)
+        p80 = int(data_count * 0.8)
+        p90 = int(data_count * 0.9)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"files": dataset[:p80]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"files": dataset[p80:p90]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"files": dataset[p90:]},
+            ),
+        ]
+
+    def _calc_label(self, path, depth, substr="/mel/"):
+        spect = substr
+        dirpath: str = os.path.dirname(path)
+        substr_index = dirpath.find(spect)
+        if substr_index < 0:
+            spect = spect.replace("/", "\\")
+            substr_index = dirpath.find(spect)
+
+        labstr = dirpath[substr_index + len(spect) :]
+        labs = labstr.split("/")
+        if len(labs) < 2:
+            labs = labstr.split("\\")
+
+        if depth <= len(labs):
+            return int(labs[depth - 1].split("_")[0])
+        else:
+            return int(labs[-1].split("_")[0])
+
+    def _generate_examples(self, files):
+        if self.config.name == "raw":
+            for i, path in enumerate(files):
+                yield i, {
+                    "audio": path["audio"],
+                    "mel": path["mel"],
+                    "fst_level_label": _NAMES_1[self._calc_label(path["mel"], 1)],
+                    "sec_level_label": _NAMES_2[self._calc_label(path["mel"], 2)],
+                    "thr_level_label": _NAMES_3[self._calc_label(path["mel"], 3)],
+                }
+
+        else:
+            for i, path in enumerate(files):
+                yield i, {
+                    "mel": path["mel"],
+                    "cqt": path["cqt"],
+                    "chroma": path["chroma"],
+                    "fst_level_label": _NAMES_1[self._calc_label(path["mel"], 1)],
+                    "sec_level_label": _NAMES_2[self._calc_label(path["mel"], 2)],
+                    "thr_level_label": _NAMES_3[self._calc_label(path["mel"], 3)],
+                }