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
+language:
+- zh
+- en
+tags:
+- music
+- art
+pretty_name: Chest voice and Falsetto Dataset
+size_categories:
+- 1K<n<10K
+viewer: false
 ---
+
+# Dataset Card for Chest voice and Falsetto Dataset
+The raw dataset, sourced from the [Chest Voice and Falsetto Dataset](https://ccmusic-database.github.io/en/database/ccm.html#shou3), includes 1,280 monophonic singing audio files in .wav format, performed, recorded, and annotated by students majoring in Vocal Music at the China Conservatory of Music. The chest voice is tagged as "chest" and the falsetto voice as "falsetto." Additionally, the dataset encompasses the Mel spectrogram, Mel frequency cepstral coefficient (MFCC), and spectral features of each audio segment, totaling 5,120 CSV files.
+
+The raw dataset did not distinguish between male and female voices, a critical detail for accurately identifying chest and falsetto vocal techniques. To correct this, we undertook a careful manual review and added gender annotations to the dataset. Following this process, we constructed the `default subset` of the current integrated version of the dataset, viewable in [viewer](https://www.modelscope.cn/datasets/ccmusic-database/chest_falsetto/dataPeview).
+
+As the default subset had not undergone evaluation, we created the `eval subset` from it to verify the integrated dataset's effectiveness and completed the evaluation, viewable at [chest_falsetto](https://www.modelscope.cn/models/ccmusic-database/chest_falsetto). Below is a brief overview of the data structure for each subset within the integrated dataset.
+
+## Dataset Structure
+<style>
+  .datastructure td {
+    vertical-align: middle !important;
+    text-align: center;
+  }
+  .datastructure th {
+    text-align: center;
+  }
+</style>
+
+### Default Subset
+<table class="datastructure">
+    <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</td>
+        <td>.jpg, 22050Hz</td>
+        <td>m_chest, m_falsetto, f_chest, f_falsetto</td>
+        <td>male, female</td>
+        <td>chest, falsetto</td>
+    </tr>
+    <tr> 
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+    </tr>
+</table>
+
+### Eval Subset
+<table class="datastructure">
+    <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, 0.496s, 22050Hz</td>
+        <td>.jpg, 0.496s, 22050Hz</td>
+        <td>.jpg, 0.496s, 22050Hz</td>
+        <td>m_chest, m_falsetto, f_chest, f_falsetto</td>
+        <td>male, female</td>
+        <td>chest, falsetto</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/chest_falsetto/repo?Revision=master&FilePath=.%2Fdata%2Ffalsetto.png&View=true">
+
+### Data Instances
+.zip(.wav, .jpg)
+
+### Data Fields
+m_chest, f_chest, m_falsetto, f_falsetto
+
+### Data Splits
+| Split(6:2:2) / Subset |   default & eval    |
+| :-------------------: | :-----------------: |
+|         train         |         767         |
+|      validation       |         256         |
+|         test          |         257         |
+|         total         |        1280         |
+|   total duration(s)   | `640.0513605442178` |
+
+## Viewer
+<https://www.modelscope.cn/datasets/ccmusic-database/chest_falsetto/dataPeview>
+
+## Usage
+### Default Subset
+```python
+from datasets import load_dataset
+
+ds = load_dataset("ccmusic-database/chest_falsetto", 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
+
+ds = load_dataset("ccmusic-database/chest_falsetto", 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/chest_falsetto
+cd chest_falsetto
+```
+
+## Dataset Description
+- **Homepage:** <https://ccmusic-database.github.io>
+- **Repository:** <https://huggingface.co/datasets/ccmusic-database/chest_falsetto>
+- **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/chest_falsetto>
+
+### Dataset Summary
+For the pre-processed version, the audio clip was into 0.25 seconds and then transformed to Mel, CQT and Chroma spectrogram in .jpg format, resulting in 8,974 files. The chest/falsetto label for each file is given as one of the four classes: m chest, m falsetto, f chest, and f falsetto. The spectrogram, the chest/falsetto label and the gender label are combined into one data entry, with the first three columns representing the Mel, CQT and Chroma. The fourth and fifth columns are the chest/falsetto label and gender label, respectively. Additionally, the integrated dataset provides the function to shuffle and split the dataset into training, validation, and test sets in an 8:1:1 ratio. This dataset can be used for singing-related tasks such as singing gender classification or chest and falsetto voice classification.
+
+### Supported Tasks and Leaderboards
+Audio classification, singing method classification, voice classification
+
+### Languages
+Chinese, English
+
+## Dataset Creation
+### Curation Rationale
+Lack of a dataset for Chest voice and Falsetto
+
+### Source Data
+#### Initial Data Collection and Normalization
+Zhaorui Liu, Monan Zhou
+
+#### Who are the source language producers?
+Students from CCMUSIC
+
+### Annotations
+#### Annotation process
+1280 monophonic singing audio (.wav format) of chest and falsetto voices, with chest voice tagged as _chest_ and falsetto voice tagged as _falsetto_.
+
+#### Who are the annotators?
+Students from CCMUSIC
+
+### 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 chest and falsetto voices
+
+### Other Known Limitations
+Recordings are cut into slices that are too short;
+The CQT spectrum column has the problem of spectrum leakage, but because the original audio slice is too short, only 0.5s, it cannot effectively avoid this problem.
+
+## Additional Information
+### Dataset Curators
+Zijin Li
+
+### Evaluation
+<https://huggingface.co/ccmusic-database/chest_falsetto>
+
+### 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 chest and falsetto voices

chest_falsetto.py

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+import os
+import random
+import datasets
+from datasets.tasks import ImageClassification
+
+_NAMES = {
+    "all": ["m_chest", "f_chest", "m_falsetto", "f_falsetto"],
+    "gender": ["female", "male"],
+    "singing_method": ["falsetto", "chest"],
+}
+
+_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 chest_falsetto(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 _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"):
+                    item_id = fname.split(".")[0]
+                    files[item_id] = {"audio": fpath}
+
+            for fpath in dl_manager.iter_files([mel_files]):
+                fname = os.path.basename(fpath)
+                if fname.endswith(".jpg"):
+                    item_id = fname.split(".")[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]):
+                if "mel" in fpath and os.path.basename(fpath).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
+            filename: str = os.path.basename(fpath)[:-4]
+            label = "_".join(filename.split("_")[1:3])
+            categories[label].append(data)
+
+        testset, validset, trainset = [], [], []
+        for cls in categories:
+            random.shuffle(categories[cls])
+            count = len(categories[cls])
+            p60 = int(count * 0.6)
+            p80 = int(count * 0.8)
+            trainset += categories[cls][:p60]
+            validset += categories[cls][p60:p80]
+            testset += categories[cls][p80:]
+
+        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, fpath in enumerate(files):
+                file_name = os.path.basename(fpath["audio"])
+                sex = file_name.split("_")[1]
+                method = file_name.split("_")[2].split(".")[0]
+                yield i, {
+                    "audio": fpath["audio"],
+                    "mel": fpath["mel"],
+                    "label": f"{sex}_{method}",
+                    "gender": "male" if sex == "m" else "female",
+                    "singing_method": method,
+                }
+
+        else:
+            for i, fpath in enumerate(files):
+                file_name: str = os.path.basename(fpath)
+                sex = file_name.split("_")[1]
+                method = file_name.split("_")[2]
+                yield i, {
+                    "mel": fpath,
+                    "cqt": fpath.replace("mel", "cqt"),
+                    "chroma": fpath.replace("mel", "chroma"),
+                    "label": f"{sex}_{method}",
+                    "gender": "male" if sex == "m" else "female",
+                    "singing_method": method,
+                }