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: Musical Instruments Timbre Evaluation Database
+size_categories:
+- n<1K
+viewer: false
 ---
+
+# Dataset Card for Chinese Musical Instruments Timbre Evaluation Database
+The raw dataset is sourced from the [National Musical Instruments Timbre Evaluation Dataset](https://ccmusic-database.github.io/en/database/ccm.html#shou4), which includes subjective timbre evaluation scores using 16 terms such as bright, dark, raspy, etc., evaluated across 37 Chinese instruments and 24 Western instruments by 14 participants with musical backgrounds in a subjective evaluation experiment. Additionally, it contains 10 spectrogram analysis reports for 10 instruments.
+
+Based on the aforementioned raw dataset, after data processing, we have constructed the `default subset` of the current integrated version of the dataset, dividing the Chinese section and the Western section into two splits. Each split consists of multiple data entries, with each entry structured across 18 columns. The Chinese split includes 37 entries, while the Western split comprises 24 entries. The first column of each data entry presents the instrument recordings in .wav format, sampled at a rate of 44,100 Hz. The second column provides the Chinese pinyin or English name of the instrument. The following 16 columns correspond to the 10-point scores of the 16 terms. This dataset is suitable for conducting timbre analysis of musical instruments and can also be utilized for various single or multiple regression tasks related to term scoring. The data structure of the default subset can be viewed in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/instrument_timbre/dataPeview). As the current dataset has been cited and used in two published articles, the construction of `eval subset` has been omitted.
+
+## Viewer
+<https://www.modelscope.cn/datasets/ccmusic-database/instrument_timbre/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>instrument_name</th>
+        <th>slim / bright / ... / turbid (16 colums)</th>
+    </tr>
+    <tr>
+        <td>.wav, 44100Hz</td>
+        <td>.jpg, 44100Hz</td>
+        <td>string</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
+Chinese traditional instruments / Western instruments
+
+### Data Splits
+Chinese, Western
+
+## Dataset Description
+- **Homepage:** <https://ccmusic-database.github.io>
+- **Repository:** <https://huggingface.co/datasets/ccmusic-database/CMITE>
+- **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/instrument_timbre>
+
+### Dataset Summary
+During the integration, we have crafted the Chinese part and the Non-Chinese part into two splits. Each split is composed of multiple data entries, with each entry structured across 18 columns. The Chinese split encompasses 37  entries, while the Non-Chinese split includes 24 entries. The premier column of each data entry presents the instrument recordings in the .wav format, sampled at a rate of 22,050 Hz. The second column provides the Chinese pinyin or English name of the instrument. The subsequent 16 columns correspond to the 10-point score of the 16 terms. This dataset is suitable for conducting timber analysis of musical instruments and can also be utilized for various single or multiple regression tasks related to term scoring.
+
+### Supported Tasks and Leaderboards
+Musical Instruments Timbre Evaluation
+
+### Languages
+Chinese, English
+
+## Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ccmusic-database/instrument_timbre")
+for item in ds["Chinese"]:
+    print(item)
+
+for item in ds["Western"]:
+    print(item)
+```
+
+## Maintenance
+```bash
+GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/instrument_timbre
+cd instrument_timbre
+```
+
+## Dataset Creation
+### Curation Rationale
+Lack of a dataset for musical instruments timbre evaluation
+
+### Source Data
+#### Initial Data Collection and Normalization
+Zhaorui Liu, Monan Zhou
+
+#### Who are the source language producers?
+Students from CCMUSIC
+
+### Annotations
+#### Annotation process
+Subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national and 24 Non-Chinese terms given by 14 participants in a subjective evaluation experiment
+
+#### 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 traditional instruments
+
+### Other Known Limitations
+Limited data
+
+## Additional Information
+### Dataset Curators
+Zijin Li
+
+### Evaluation
+#### For Chinese instruments
+[Yiliang, J. et al. (2020) ‘Analysis of Chinese Musical Instrument Timbre Based on Objective Features’, Journal of Fudan University(Natural Science), pp. 346-353+359. doi:10.15943/j.cnki.fdxb-jns.2020.03.014.](https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2020&filename=FDXB202003014&uniplatform=NZKPT&v=85qLeLUyrDt%25mmd2Btak%25mmd2BN90N7vYZSv%25mmd2BVc1EfPmaYcvpvrgY1XkL215gYG4J%25mmd2FD09viR0w)
+#### For Non-Chinese instruments
+[Jiang, Wei et al. “Analysis and Modeling of Timbre Perception Features of Chinese Musical Instruments.” 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS) (2019): 191-195.](https://ieeexplore.ieee.org/document/8940168)
+
+### 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 musical instruments' timbre evaluation

instrument_timbre.py

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+import os
+import datasets
+import pandas as pd
+from datasets.tasks import AudioClassification
+
+
+_NAMES = [
+    # Chinese 0-36
+    "gao_hu",
+    "er_hu",
+    "zhong_hu",
+    "ge_hu",
+    "di_yin_ge_hu",
+    "jing_hu",
+    "ban_hu",
+    "bang_di",
+    "qu_di",
+    "xin_di",
+    "da_di",
+    "gao_yin_sheng",
+    "zhong_yin_sheng",
+    "di_yin_sheng",
+    "gao_yin_suo_na",
+    "zhong_yin_suo_na",
+    "ci_zhong_yin_suo_na",
+    "di_yin_suo_na",
+    "gao_yin_guan",
+    "zhong_yin_guan",
+    "di_yin_guan",
+    "bei_di_yin_guan",
+    "ba_wu",
+    "xun",
+    "xiao",
+    "liu_qin",
+    "xiao_ruan",
+    "pi_pa",
+    "yang_qin",
+    "zhong_ruan",
+    "da_ruan",
+    "gu_zheng",
+    "gu_qin",
+    "kong_hou",
+    "san_xian",
+    "yun_luo",
+    "bian_zhong",
+    # Western 37-60
+    "violin",
+    "viola",
+    "cello",
+    "double_bass",
+    "piccolo",
+    "flute",
+    "oboe",
+    "clarinet",
+    "bassoon",
+    "saxophone",
+    "trumpet",
+    "trombone",
+    "horn",
+    "tuba",
+    "harp",
+    "tubular_bells",
+    "bells",
+    "xylophone",
+    "vibraphone",
+    "marimba",
+    "piano",
+    "clavichord",
+    "accordion",
+    "organ",
+]
+
+_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",
+    "Chinese": f"{_DOMAIN}/Chinese.csv",
+    "Western": f"{_DOMAIN}/Western.csv",
+}
+
+
+class instrument_timbre(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "audio": datasets.Audio(sampling_rate=44100),
+                    "mel": datasets.Image(),
+                    "instrument": datasets.features.ClassLabel(names=_NAMES),
+                    "slim": datasets.Value("float32"),
+                    "bright": datasets.Value("float32"),
+                    "dim": datasets.Value("float32"),
+                    "sharp": datasets.Value("float32"),
+                    "thick": datasets.Value("float32"),
+                    "thin": datasets.Value("float32"),
+                    "solid": datasets.Value("float32"),
+                    "clear": datasets.Value("float32"),
+                    "dry": datasets.Value("float32"),
+                    "plump": datasets.Value("float32"),
+                    "rough": datasets.Value("float32"),
+                    "pure": datasets.Value("float32"),
+                    "hoarse": datasets.Value("float32"),
+                    "harmonious": datasets.Value("float32"),
+                    "soft": datasets.Value("float32"),
+                    "turbid": datasets.Value("float32"),
+                }
+            ),
+            supervised_keys=("audio", "instrument"),
+            homepage=_HOMEPAGE,
+            license="CC-BY-NC-ND",
+            version="1.2.0",
+            task_templates=[
+                AudioClassification(
+                    task="audio-classification",
+                    audio_column="audio",
+                    label_column="instrument",
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        audio_files = dl_manager.download_and_extract(_URLS["audio"])
+        mel_files = dl_manager.download_and_extract(_URLS["mel"])
+        cn_ins_eval = dl_manager.download(_URLS["Chinese"])
+        en_ins_eval = dl_manager.download(_URLS["Western"])
+        cn_labels = pd.read_csv(cn_ins_eval, index_col="instrument_id")
+        en_labels = pd.read_csv(en_ins_eval, index_col="instrument_id")
+        cn_dataset, en_dataset = {}, {}
+        for path in dl_manager.iter_files([audio_files]):
+            fname: str = os.path.basename(path)
+            i = int(fname.split(".wa")[0]) - 1
+            if fname.endswith(".wav"):
+                region = os.path.basename(os.path.dirname(path))
+                labels = cn_labels if region == "Chinese" else en_labels
+                data = {
+                    "audio": path,
+                    "mel": "",
+                    "instrument": labels.iloc[i]["instrument_name"],
+                    "slim": labels.iloc[i]["slim"],
+                    "bright": labels.iloc[i]["bright"],
+                    "dim": labels.iloc[i]["dim"],
+                    "sharp": labels.iloc[i]["sharp"],
+                    "thick": labels.iloc[i]["thick"],
+                    "thin": labels.iloc[i]["thin"],
+                    "solid": labels.iloc[i]["solid"],
+                    "clear": labels.iloc[i]["clear"],
+                    "dry": labels.iloc[i]["dry"],
+                    "plump": labels.iloc[i]["plump"],
+                    "rough": labels.iloc[i]["rough"],
+                    "pure": labels.iloc[i]["pure"],
+                    "hoarse": labels.iloc[i]["hoarse"],
+                    "harmonious": labels.iloc[i]["harmonious"],
+                    "soft": labels.iloc[i]["soft"],
+                    "turbid": labels.iloc[i]["turbid"],
+                }
+                if region == "Chinese":
+                    cn_dataset[i] = data
+                else:
+                    en_dataset[i] = data
+
+        for path in dl_manager.iter_files([mel_files]):
+            fname = os.path.basename(path)
+            i = int(fname.split(".jp")[0]) - 1
+            if fname.endswith(".jpg"):
+                if os.path.basename(os.path.dirname(path)) == "Chinese":
+                    cn_dataset[i]["mel"] = path
+                else:
+                    en_dataset[i]["mel"] = path
+
+        return [
+            datasets.SplitGenerator(
+                name="Chinese",
+                gen_kwargs={
+                    "files": [cn_dataset[k] for k in sorted(cn_dataset)],
+                },
+            ),
+            datasets.SplitGenerator(
+                name="Western",
+                gen_kwargs={
+                    "files": [en_dataset[k] for k in sorted(en_dataset)],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, files):
+        for i, path in enumerate(files):
+            yield i, path