upl scripts

.gitignore

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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: Bel Conto and Chinese Folk Song Singing Tech
+size_categories:
+- 1K<n<10K
+viewer: false
 ---
+
+# Dataset Card for Bel Conto and Chinese Folk Song Singing Tech
+The raw dataset, sourced from the [Bel Canto and National Singing Dataset](https://ccmusic-database.github.io/en/database/ccm.html#shou9), contains 203 acapella singing clips performed in two styles, Bel Canto and Chinese folk singing style, by professional vocalists. All of them are sung by professional vocalists and were recorded in professional commercial recording studios.
+
+Based on the aforementioned raw dataset, we have constructed the `default subset` of the current integrated version of the dataset, and its data structure can be viewed in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/bel_canto/dataPeview).
+
+Since the default subset has not been evaluated, to verify its effectiveness, we have built the `eval subset` based on the default subset for the evaluation of the integrated version of the dataset. The evaluation results can be seen in the [bel_canto](https://www.modelscope.cn/models/ccmusic-database/bel_canto). Below are the data structures and invocation methods of the subsets.
+
+## Dataset Structure
+<style>
+  .belcanto td {
+    vertical-align: middle !important;
+    text-align: center;
+  }
+  .belcanto th {
+    text-align: center;
+  }
+</style>
+
+### Default Subset
+<table class="belcanto">
+    <tr>
+        <th>audio</th>
+        <th>mel (spectrogram)</th>
+        <th>label (4-class)</th>
+        <th>gender (2-class)</th>
+        <th>singing_method(2-class)</th>
+    </tr>
+    <tr>
+        <td>.wav, 22050Hz</audio></td>
+        <td>.jpg, 22050Hz</td>
+        <td>m_bel, f_bel, m_folk, f_folk</td>
+        <td>male, female</td>
+        <td>Folk_Singing, Bel_Canto</td>
+    </tr>
+    <tr>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+    </tr>
+</table>
+
+### Eval Subset
+<table class="belcanto">
+    <tr>
+        <th>mel</th>
+        <th>cqt</th>
+        <th>chroma</th>
+        <th>label (4-class)</th>
+        <th>gender (2-class)</th>
+        <th>singing_method (2-class)</th>
+    </tr>
+    <tr>
+        <td>.jpg, 1.6s, 22050Hz</td>
+        <td>.jpg, 1.6s, 22050Hz</td>
+        <td>.jpg, 1.6s, 22050Hz</td>
+        <td>m_bel, f_bel, m_folk, f_folk</td>
+        <td>male, female</td>
+        <td>Folk_Singing, Bel_Canto</td>
+    </tr>
+    <tr>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+    </tr>
+</table>
+
+<img src="https://www.modelscope.cn/api/v1/datasets/ccmusic-database/bel_canto/repo?Revision=master&FilePath=.%2Fdata%2Fbel.png&View=true">
+
+### Data Instances
+.zip(.wav, .jpg)
+
+### Data Fields
+m_bel, f_bel, m_folk, f_folk
+
+### Data Splits 
+| Split(8:1:1) / Subset |       default       |        eval         |
+| :-------------------: | :-----------------: | :-----------------: |
+|         train         |         159         |        7907         |
+|      validation       |         20          |         988         |
+|         test          |         23          |         991         |
+|         total         |         202         |        9886         |
+|   total duration(s)   | `18192.37652721089` | `18192.37652721089` |
+
+## Viewer
+<https://www.modelscope.cn/datasets/ccmusic-database/bel_canto/dataPeview>
+
+## Usage
+### Default Subset
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ccmusic-database/bel_canto", 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/bel_canto", 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/bel_canto
+cd bel_canto
+```
+
+## Dataset Description
+- **Homepage:** <https://ccmusic-database.github.io>
+- **Repository:** <https://huggingface.co/datasets/ccmusic-database/bel_canto>
+- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
+- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
+- **Point of Contact:** <https://www.modelscope.cn/datasets/ccmusic-database/bel_canto>
+
+### Dataset Summary
+This database contains hundreds of acapella singing clips that are sung in two styles, Bel Conto and Chinese national singing style by professional vocalists. All of them are sung by professional vocalists and were recorded in professional commercial recording studios.
+
+### Supported Tasks and Leaderboards
+Audio classification, Image classification, singing method classification, voice classification
+
+### Languages
+Chinese, English
+
+## Dataset Creation
+### Curation Rationale
+Lack of a dataset for Bel Conto and Chinese folk song singing tech
+
+### Source Data
+#### Initial Data Collection and Normalization
+Zhaorui Liu, Monan Zhou
+
+#### Who are the source language producers?
+Students from CCMUSIC
+
+### Annotations
+#### Annotation process
+All of them are sung by professional vocalists and were recorded in professional commercial recording studios.
+
+#### Who are the annotators?
+professional vocalists
+
+### Personal and Sensitive Information
+None
+
+## Considerations for Using the Data
+### Social Impact of Dataset
+Promoting the development of AI in the music industry
+
+### Discussion of Biases
+Only for Chinese songs
+
+### Other Known Limitations
+Some singers may not have enough professional training in classical or ethnic vocal techniques.
+
+## Additional Information
+### Dataset Curators
+Zijin Li
+
+### Evaluation
+<https://huggingface.co/ccmusic-database/bel_canto>
+
+### 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 distinguishing Bel Conto and Chinese folk song singing tech

bel_canto.py

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+import os
+import random
+import hashlib
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_NAMES = {
+    "all": ["m_bel", "f_bel", "m_folk", "f_folk"],
+    "gender": ["female", "male"],
+    "singing_method": ["Folk_Singing", "Bel_Canto"],
+}
+
+_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"
+
+
+_URLS = {
+    "audio": f"{_DOMAIN}/audio.zip",
+    "mel": f"{_DOMAIN}/mel.zip",
+    "eval": f"{_DOMAIN}/eval.zip",
+}
+
+
+class bel_canto(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=(
+                datasets.Features(
+                    {
+                        "audio": datasets.Audio(sampling_rate=22050),
+                        "mel": datasets.Image(),
+                        "label": datasets.features.ClassLabel(names=_NAMES["all"]),
+                        "gender": datasets.features.ClassLabel(names=_NAMES["gender"]),
+                        "singing_method": datasets.features.ClassLabel(
+                            names=_NAMES["singing_method"]
+                        ),
+                    }
+                )
+                if self.config.name == "default"
+                else (
+                    datasets.Features(
+                        {
+                            "mel": datasets.Image(),
+                            "cqt": datasets.Image(),
+                            "chroma": datasets.Image(),
+                            "label": datasets.features.ClassLabel(names=_NAMES["all"]),
+                            "gender": datasets.features.ClassLabel(
+                                names=_NAMES["gender"]
+                            ),
+                            "singing_method": datasets.features.ClassLabel(
+                                names=_NAMES["singing_method"]
+                            ),
+                        }
+                    )
+                )
+            ),
+            supervised_keys=("mel", "label"),
+            homepage=_HOMEPAGE,
+            license="CC-BY-NC-ND",
+            version="1.2.0",
+            task_templates=[
+                ImageClassification(
+                    task="image-classification",
+                    image_column="mel",
+                    label_column="label",
+                )
+            ],
+        )
+
+    def _str2md5(self, original_string: str):
+        md5_obj = hashlib.md5()
+        md5_obj.update(original_string.encode("utf-8"))
+        return md5_obj.hexdigest()
+
+    def _split_generators(self, dl_manager):
+        dataset = []
+        if self.config.name == "default":
+            files = {}
+            audio_files = dl_manager.download_and_extract(_URLS["audio"])
+            mel_files = dl_manager.download_and_extract(_URLS["mel"])
+            for fpath in dl_manager.iter_files([audio_files]):
+                fname: str = os.path.basename(fpath)
+                if fname.endswith(".wav"):
+                    cls = os.path.basename(os.path.dirname(fpath)) + "/"
+                    item_id = self._str2md5(cls + fname.split(".wa")[0])
+                    files[item_id] = {"audio": fpath}
+
+            for fpath in dl_manager.iter_files([mel_files]):
+                fname = os.path.basename(fpath)
+                if fname.endswith(".jpg"):
+                    cls = os.path.basename(os.path.dirname(fpath)) + "/"
+                    item_id = self._str2md5(cls + fname.split(".jp")[0])
+                    files[item_id]["mel"] = fpath
+
+            dataset = list(files.values())
+
+        else:
+            data_files = dl_manager.download_and_extract(_URLS["eval"])
+            for fpath in dl_manager.iter_files([data_files]):
+                fname = os.path.basename(fpath)
+                if "mel" in fpath and fname.endswith(".jpg"):
+                    dataset.append(fpath)
+
+        categories = {}
+        for name in _NAMES["all"]:
+            categories[name] = []
+
+        for data in dataset:
+            fpath = data["audio"] if self.config.name == "default" else data
+            label = os.path.basename(os.path.dirname(fpath))
+            categories[label].append(data)
+
+        testset, validset, trainset = [], [], []
+        for cls in categories:
+            random.shuffle(categories[cls])
+            count = len(categories[cls])
+            p80 = int(count * 0.8)
+            p90 = int(count * 0.9)
+            trainset += categories[cls][:p80]
+            validset += categories[cls][p80:p90]
+            testset += categories[cls][p90:]
+
+        random.shuffle(trainset)
+        random.shuffle(validset)
+        random.shuffle(testset)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"files": trainset}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"files": validset}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"files": testset}
+            ),
+        ]
+
+    def _generate_examples(self, files):
+        if self.config.name == "default":
+            for i, item in enumerate(files):
+                label: str = os.path.basename(os.path.dirname(item["audio"]))
+                yield i, {
+                    "audio": item["audio"],
+                    "mel": item["mel"],
+                    "label": label,
+                    "gender": ("male" if label.split("_")[0] == "m" else "female"),
+                    "singing_method": (
+                        "Bel_Canto" if label.split("_")[1] == "bel" else "Folk_Singing"
+                    ),
+                }
+
+        else:
+            for i, fpath in enumerate(files):
+                label = os.path.basename(os.path.dirname(fpath))
+                yield i, {
+                    "mel": fpath,
+                    "cqt": fpath.replace("mel", "cqt"),
+                    "chroma": fpath.replace("mel", "chroma"),
+                    "label": label,
+                    "gender": ("male" if label.split("_")[0] == "m" else "female"),
+                    "singing_method": (
+                        "Bel_Canto" if label.split("_")[1] == "bel" else "Folk_Singing"
+                    ),
+                }