upl all codes

.gitignore

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

README.md

@@ -104,8 +104,18 @@ This dataset card aims to be a base template for new datasets. It has been gener
 [More Information Needed]
 
 ### Citation Information
-
-[More Information Needed]
+```
+@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}
+}
+```
 
 ### Contributions
 

chest_falsetto.py

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+import os
+import random
+import datasets
+from datasets.tasks import AudioClassification
+
+
+# Once upload a new piano brand, please register its name here
+_NAMES = ['m_chest', 'f_chest', 'm_falsetto', 'f_falsetto']
+
+_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 1280 monophonic singing audio (.wav format) of chest and falsetto voices, 
+with chest voice tagged as _chest and falsetto voice tagged as _falsetto. In addition, 
+the Mel-spectrogram, MFCC, and spectral characteristics of each audio segment are also included, 
+for a total of 5120 CSV files.
+"""
+
+_URL = _HOMEPAGE + "/resolve/main/data/client_data.zip"
+
+
+class chest_falsetto(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "audio": datasets.Audio(sampling_rate=44_100),
+                    "label": datasets.features.ClassLabel(names=_NAMES),
+                    "gender": datasets.Value("string"),
+                    "singing_method": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=("audio", "label"),
+            homepage=_HOMEPAGE,
+            license="mit",
+            citation=_CITATION,
+            description=_DESCRIPTION,
+            task_templates=[
+                AudioClassification(
+                    task="audio-classification",
+                    audio_column="audio",
+                    label_column="label",
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(_URL)
+        files = dl_manager.iter_files([data_files])
+        trainset, validationset, testset = [], [], []
+
+        for _, path in enumerate(files):
+            trainset.append(path)
+
+        random.shuffle(trainset)
+        data_count = len(trainset)
+        p80 = int(data_count * 0.8)
+        p90 = int(data_count * 0.9)
+        validationset = trainset[p80:p90]
+        testset = trainset[p90:]
+        trainset = trainset[:p80]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": trainset,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "files": validationset,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "files": testset,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, files):
+        for i, path in enumerate(files):
+            file_name = os.path.basename(path)
+            if file_name.endswith(".wav"):
+                sex = file_name.split('_')[1]
+                gender = 'male' if sex == 'm' else 'female'
+                method = file_name.split('_')[2][:-4]
+                yield i, {
+                    "audio": path,
+                    "label": sex + '_' + method,
+                    "gender": gender,
+                    "singing_method": method,
+                }

data/client_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:82a14d2259d441db6c84a9a4417468da6b5bcd89b3e95cb5c28e829fa12222f7
+size 39271503