upl data and basic codes

.gitignore

@@ -0,0 +1,2 @@
+rename.sh
+test.py

CMITE.py

@@ -0,0 +1,116 @@
+import os
+import datasets
+import pandas as pd
+from datasets.tasks import AudioClassification
+
+
+# Once upload a new piano brand, please register its name here
+_NAMES = ['instrument' + str(i) for i in range(1, 38)]
+
+_DBNAME = os.path.basename(__file__).split('.')[0]
+
+_HOMEPAGE = "https://huggingface.co/datasets/ccmusic-database/" + _DBNAME
+
+_CITATION = """\
+@dataset{zhaorui_liu_2021_5676893,
+  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu and Zijin Li},
+  title        = {{Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)}},
+  month        = nov,
+  year         = 2021,
+  publisher    = {Zenodo},
+  version      = {1.1},
+  doi          = {10.5281/zenodo.5676893},
+  url          = {https://doi.org/10.5281/zenodo.5676893}
+}
+"""
+
+_DESCRIPTION = """\
+This database contains subjective timbre evaluation scores of 16 subjective timbre evaluation terms 
+(such as bright, dark, raspy) on 37 Chinese national terms given by 14 participants in a subjective evaluation experiment. 
+Furthermore, 10 reports on spectrum analysis of 10 instruments are also included.
+"""
+
+_URL = _HOMEPAGE + "/resolve/main/data/instruments.zip"
+
+_CSV = _HOMEPAGE + "/resolve/main/data/scoring.csv"
+
+
+class CMITE(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "audio": datasets.Audio(sampling_rate=44_100),
+                    "instrument_id": datasets.features.ClassLabel(names=_NAMES),
+                    "Chinese_name": datasets.Value("string"),
+                    "slim": datasets.Value("float64"),
+                    "bright": datasets.Value("float64"),
+                    "dim": datasets.Value("float64"),
+                    "sharp": datasets.Value("float64"),
+                    "thick": datasets.Value("float64"),
+                    "thin": datasets.Value("float64"),
+                    "solid": datasets.Value("float64"),
+                    "clear": datasets.Value("float64"),
+                    "dry": datasets.Value("float64"),
+                    "plump": datasets.Value("float64"),
+                    "rough": datasets.Value("float64"),
+                    "pure": datasets.Value("float64"),
+                    "hoarse": datasets.Value("float64"),
+                    "harmonious": datasets.Value("float64"),
+                    "soft": datasets.Value("float64"),
+                    "turbid": datasets.Value("float64"),
+                }
+            ),
+            supervised_keys=("audio", "instrument_id"),
+            homepage=_HOMEPAGE,
+            license="mit",
+            citation=_CITATION,
+            description=_DESCRIPTION,
+            task_templates=[
+                AudioClassification(
+                    task="audio-classification",
+                    audio_column="audio",
+                    label_column="instrument_id",
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files]),
+                    "labels": dl_manager.download(_CSV),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, files, labels):
+        ins_eval = pd.read_csv(labels, index_col='instrument_id')
+        for _, path in enumerate(files):
+            file_name = os.path.basename(path)
+            if file_name.endswith(".wav"):
+                i = int(file_name.split('.wav')[0])
+                yield i, {
+                    "audio": path,
+                    "instrument_id": "instrument" + str(i),
+                    "Chinese_name": ins_eval.iloc[i]['Chinese_name'],
+                    "slim": ins_eval.iloc[i]['slim'],
+                    "bright": ins_eval.iloc[i]['bright'],
+                    "dim": ins_eval.iloc[i]['dim'],
+                    "sharp": ins_eval.iloc[i]['sharp'],
+                    "thick": ins_eval.iloc[i]['thick'],
+                    "thin": ins_eval.iloc[i]['thin'],
+                    "solid": ins_eval.iloc[i]['solid'],
+                    "clear": ins_eval.iloc[i]['clear'],
+                    "dry": ins_eval.iloc[i]['dry'],
+                    "plump": ins_eval.iloc[i]['plump'],
+                    "rough": ins_eval.iloc[i]['rough'],
+                    "pure": ins_eval.iloc[i]['pure'],
+                    "hoarse": ins_eval.iloc[i]['hoarse'],
+                    "harmonious": ins_eval.iloc[i]['harmonious'],
+                    "soft": ins_eval.iloc[i]['soft'],
+                    "turbid": ins_eval.iloc[i]['turbid'],
+                }

README.md

@@ -12,7 +12,7 @@ pretty_name: Chinese Musical Instruments Timbre Evaluation Database
 size_categories:
 - n<1K
 ---
-# Dataset Card for Dataset Name
+# Dataset Card for Chinese Musical Instruments Timbre Evaluation Database
 
 ## Dataset Description
 
@@ -23,8 +23,7 @@ size_categories:
 - **Point of Contact:** 
 
 ### Dataset Summary
-
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+This database contains subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national terms given by 14 participants in a subjective evaluation experiment. Furthermore, 10 reports on spectrum analysis of 10 instruments are also included. 
 
 ### Supported Tasks and Leaderboards
 

data/instruments.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5c26b74de97bdfefb2643fdb150daa13e3eef07bcae4b4c79d3b70d5da4fa8ed
+size 35185210

data/scoring.csv

@@ -0,0 +1,38 @@
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