upd md

.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
+- table-question-answering
+- summarization
+language:
+- zh
+- en
+tags:
+- music
+- art
+pretty_name: Acapella Evaluation Dataset
+size_categories:
+- n<1K
+viewer: false
 ---
+
+# Dataset Card for Acapella Evaluation
+The raw dataset, sourced from the [Acapella Evaluation Dataset](https://ccmusic-database.github.io/en/database/ccm.html#shou2), comprises six Mandarin pop song segments performed by 22 singers, resulting in a total of 132 audio clips. Each segment includes both a verse and a chorus. Four judges from the China Conservatory of Music assess the singing across 9 dimensions: pitch, rhythm, vocal range, timbre, pronunciation, vibrato, dynamics, breath control, and overall performance, using a 10-point scale. The evaluations are recorded in an Excel spreadsheet in .xls format.
+
+Due to the raw dataset comprising separate files for audio recordings and evaluation sheets, which hindered efficient data retrieval, we combined the original vocal recordings with their corresponding evaluation sheets to construct the `default subset` of the current integrated version of the dataset. The data structure can be viewed in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/acapella/dataPeview). The current dataset is already endorsed by published articles, hence there is no need to construct the `eval subset`.
+
+## Viewer
+<https://www.modelscope.cn/datasets/ccmusic-database/acapella/dataPeview>
+
+## Dataset Structure
+<style>
+  .datastructure td {
+    vertical-align: middle !important;
+    text-align: center;
+  }
+  .datastructure th {
+    text-align: center;
+  }
+</style>
+<table class="datastructure">
+    <tr>
+        <th>audio</th>
+        <th>mel</th>
+        <th>singer_id</th>
+        <th>pitch / rhythm / ... / overall_performance (9 colums)</th>
+    </tr>
+    <tr>
+        <td>.wav, 48000Hz</td>
+        <td>.jpg, 48000Hz</td>
+        <td>int</td>
+        <td>float(0-10)</td>
+    </tr>
+    <tr>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+    </tr>
+</table>
+
+### Data Instances
+.zip(.wav), .csv
+
+### Data Fields
+song, singer id, pitch, rhythm, vocal range, timbre, pronunciation, vibrato, dynamic, breath control and overall performance
+
+### Data Splits
+song1-6
+
+## Dataset Description
+- **Homepage:** <https://ccmusic-database.github.io>
+- **Repository:** <https://huggingface.co/datasets/ccmusic-database/acapella_evaluation>
+- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
+- **Leaderboard:** <https://www.modelscope.cn/datasets/ccmusic-database/acapella>
+- **Point of Contact:** <https://www.mdpi.com/2076-3417/12/19/9931>
+
+### Dataset Summary
+Due to the original dataset comprising separate files for audio recordings and evaluation sheets, which hindered efficient data retrieval, we have consolidated the raw vocal recordings with their corresponding assessments. The dataset is divided into six segments, each representing a different song, resulting in a total of six divisions. Each segment contains 22 entries, with each entry detailing the vocal recording of an individual singer sampled at 22,050 Hz, the singer's ID, and evaluations across the nine dimensions previously mentioned. Consequently, each entry encompasses 11 columns of data. This dataset is well-suited for tasks such as vocal analysis and regression-based singing voice rating. For instance, as previously stated, the final column of each entry denotes the overall performance score, allowing the audio to be utilized as data and this score to serve as the label for regression analysis.
+
+### Supported Tasks and Leaderboards
+Acapella evaluation/scoring
+
+### Languages
+Chinese, English
+
+## Maintenance
+```bash
+GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/acapella
+cd acapella
+```
+
+## Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ccmusic-database/acapella", subset="default")
+for i in range(1, 7):
+    for item in dataset[f"song{i}"]:
+        print(item)
+```
+
+## Dataset Creation
+### Curation Rationale
+Lack of a training dataset for the acapella scoring system
+
+### Source Data
+#### Initial Data Collection and Normalization
+Zhaorui Liu, Monan Zhou
+
+#### Who are the source language producers?
+Students and judges from CCMUSIC
+
+### Annotations
+#### Annotation process
+6 Mandarin song segments were sung by 22 singers, totaling 132 audio clips. Each segment consists of a verse and a chorus. Four judges evaluate the singing from nine aspects which are pitch, rhythm, vocal range, timbre, pronunciation, vibrato, dynamic, breath control and overall performance on a 10-point scale. The scores are recorded on a sheet.
+
+#### Who are the annotators?
+Judges from CCMUSIC
+
+### Personal and Sensitive Information
+Singers' and judges' names are hided
+
+## Considerations for Using the Data
+### Social Impact of Dataset
+Providing a training dataset for the acapella scoring system may improve the development of related Apps
+
+### Discussion of Biases
+Only for Mandarin songs
+
+### Other Known Limitations
+No starting point has been marked for the vocal
+
+## Additional Information
+### Dataset Curators
+Zijin Li
+
+### Evaluation
+[Li, R.; Zhang, M. Singing-Voice Timbre Evaluations Based on Transfer Learning. Appl. Sci. 2022, 12, 9931. https://doi.org/10.3390/app12199931](https://www.mdpi.com/2076-3417/12/19/9931)
+
+### 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 training dataset for the acapella scoring system

acapella.py

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+import os
+import datasets
+import pandas as pd
+from datasets.tasks import AudioClassification
+
+
+_NAMES = {
+    "songs": [f"song{i}" for i in range(1, 7)],
+    "singers": [f"singer{i}" for i in range(1, 23)],
+}
+
+_DBNAME = os.path.basename(__file__).split(".")[0]
+
+_DOMAIN = f"https://www.modelscope.cn/api/v1/datasets/ccmusic-database/{_DBNAME}/repo?Revision=master&FilePath=data"
+
+_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{_DBNAME}"
+
+
+_URLS = {
+    "audio": f"{_DOMAIN}/audio.zip",
+    "mel": f"{_DOMAIN}/mel.zip",
+}
+
+
+class acapella(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "audio": datasets.Audio(sampling_rate=48000),
+                    "mel": datasets.Image(),
+                    "singer_id": datasets.features.ClassLabel(names=_NAMES["singers"]),
+                    "pitch": datasets.Value("float32"),
+                    "rhythm": datasets.Value("float32"),
+                    "vocal_range": datasets.Value("float32"),
+                    "timbre": datasets.Value("float32"),
+                    "pronunciation": datasets.Value("float32"),
+                    "vibrato": datasets.Value("float32"),
+                    "dynamic": datasets.Value("float32"),
+                    "breath_control": datasets.Value("float32"),
+                    "overall_performance": datasets.Value("float32"),
+                }
+            ),
+            supervised_keys=("audio", "singer_id"),
+            homepage=_HOMEPAGE,
+            license="CC-BY-NC-ND",
+            version="1.2.0",
+            task_templates=[
+                AudioClassification(
+                    task="audio-classification",
+                    audio_column="audio",
+                    label_column="singer_id",
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        songs = {}
+        for index in _NAMES["songs"]:
+            csv_files = dl_manager.download(f"{_DOMAIN}/{index}.csv")
+            song_eval = pd.read_csv(csv_files, index_col="singer_id")
+            scores = []
+            for i in range(22):
+                scores.append(
+                    {
+                        "pitch": song_eval.iloc[i]["pitch"],
+                        "rhythm": song_eval.iloc[i]["rhythm"],
+                        "vocal_range": song_eval.iloc[i]["vocal_range"],
+                        "timbre": song_eval.iloc[i]["timbre"],
+                        "pronunciation": song_eval.iloc[i]["pronunciation"],
+                        "vibrato": song_eval.iloc[i]["vibrato"],
+                        "dynamic": song_eval.iloc[i]["dynamic"],
+                        "breath_control": song_eval.iloc[i]["breath_control"],
+                        "overall_performance": song_eval.iloc[i]["overall_performance"],
+                    }
+                )
+
+            songs[index] = scores
+
+        audio_files = dl_manager.download_and_extract(_URLS["audio"])
+        for fpath in dl_manager.iter_files([audio_files]):
+            fname: str = os.path.basename(fpath)
+            if fname.endswith(".wav"):
+                song_id = os.path.basename(os.path.dirname(fpath))
+                singer_id = int(fname.split("(")[1].split(")")[0]) - 1
+                songs[song_id][singer_id]["audio"] = fpath
+
+        mel_files = dl_manager.download_and_extract(_URLS["mel"])
+        for fpath in dl_manager.iter_files([mel_files]):
+            fname = os.path.basename(fpath)
+            if fname.endswith(".jpg"):
+                song_id = os.path.basename(os.path.dirname(fpath))
+                singer_id = int(fname.split("(")[1].split(")")[0]) - 1
+                songs[song_id][singer_id]["mel"] = fpath
+
+        split_generator = []
+        for key in songs.keys():
+            split_generator.append(
+                datasets.SplitGenerator(
+                    name=key,
+                    gen_kwargs={"files": songs[key]},
+                )
+            )
+
+        return split_generator
+
+    def _generate_examples(self, files):
+        for i, item in enumerate(files):
+            yield i, {
+                "audio": item["audio"],
+                "mel": item["mel"],
+                "singer_id": i,
+                "pitch": item["pitch"],
+                "rhythm": item["rhythm"],
+                "vocal_range": item["vocal_range"],
+                "timbre": item["timbre"],
+                "pronunciation": item["pronunciation"],
+                "vibrato": item["vibrato"],
+                "dynamic": item["dynamic"],
+                "breath_control": item["breath_control"],
+                "overall_performance": item["overall_performance"],
+            }