Datasets:

ccmusic-database
/

pianos

@@ -1,270 +1,256 @@
-import os
-import random
-import datasets
-from datasets.tasks import ImageClassification, AudioClassification
-_NAMES = [
-    "PearlRiver",
-    "YoungChang",
-    "Steinway-T",
-    "Hsinghai",
-    "Kawai",
-    "Steinway",
-    "Kawai-G",
-    "Yamaha",
-]
-_DBNAME = os.path.basename(__file__).split(".")[0]
-_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic/{_DBNAME}"
-_DOMAIN = f"https://www.modelscope.cn/api/v1/datasets/ccmusic/{_DBNAME}/repo?Revision=master&FilePath=data"
-_CITATION = """\
-@dataset{zhaorui_liu_2021_5676893,
-  author       = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Zijin Li},
-  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}
-}
-"""
-_DESCRIPTION = """\
-Piano-Sound-Quality is a dataset of piano sound. It consists of 8 kinds of pianos including PearlRiver, YoungChang, Steinway-T, Hsinghai, Kawai, Steinway, Kawai-G, Yamaha(recorded by Shaohua Ji with SONY PCM-D100). Data was annotated by students from the China Conservatory of Music (CCMUSIC) in Beijing and collected by Monan Zhou.
-"""
-_PITCHES = {
-    "009": "A2",
-    "010": "A2#/B2b",
-    "011": "B2",
-    "100": "C1",
-    "101": "C1#/D1b",
-    "102": "D1",
-    "103": "D1#/E1b",
-    "104": "E1",
-    "105": "F1",
-    "106": "F1#/G1b",
-    "107": "G1",
-    "108": "G1#/A1b",
-    "109": "A1",
-    "110": "A1#/B1b",
-    "111": "B1",
-    "200": "C",
-    "201": "C#/Db",
-    "202": "D",
-    "203": "D#/Eb",
-    "204": "E",
-    "205": "F",
-    "206": "F#/Gb",
-    "207": "G",
-    "208": "G#/Ab",
-    "209": "A",
-    "210": "A#/Bb",
-    "211": "B",
-    "300": "c",
-    "301": "c#/db",
-    "302": "d",
-    "303": "d#/eb",
-    "304": "e",
-    "305": "f",
-    "306": "f#/gb",
-    "307": "g",
-    "308": "g#/ab",
-    "309": "a",
-    "310": "a#/bb",
-    "311": "b",
-    "400": "c1",
-    "401": "c1#/d1b",
-    "402": "d1",
-    "403": "d1#/e1b",
-    "404": "e1",
-    "405": "f1",
-    "406": "f1#/g1b",
-    "407": "g1",
-    "408": "g1#/a1b",
-    "409": "a1",
-    "410": "a1#/b1b",
-    "411": "b1",
-    "500": "c2",
-    "501": "c2#/d2b",
-    "502": "d2",
-    "503": "d2#/e2b",
-    "504": "e2",
-    "505": "f2",
-    "506": "f2#/g2b",
-    "507": "g2",
-    "508": "g2#/a2b",
-    "509": "a2",
-    "510": "a2#/b2b",
-    "511": "b2",
-    "600": "c3",
-    "601": "c3#/d3b",
-    "602": "d3",
-    "603": "d3#/e3b",
-    "604": "e3",
-    "605": "f3",
-    "606": "f3#/g3b",
-    "607": "g3",
-    "608": "g3#/a3b",
-    "609": "a3",
-    "610": "a3#/b3b",
-    "611": "b3",
-    "700": "c4",
-    "701": "c4#/d4b",
-    "702": "d4",
-    "703": "d4#/e4b",
-    "704": "e4",
-    "705": "f4",
-    "706": "f4#/g4b",
-    "707": "g4",
-    "708": "g4#/a4b",
-    "709": "a4",
-    "710": "a4#/b4b",
-    "711": "b4",
-    "800": "c5",
-}
-_URLS = {
-    "audio": f"{_DOMAIN}/audio.zip",
-    "mel": f"{_DOMAIN}/mel.zip",
-    "eval": f"{_DOMAIN}/eval.zip",
-}
-class pianos_Config(datasets.BuilderConfig):
-    def __init__(self, features, supervised_keys, task_templates, **kwargs):
-        super(pianos_Config, self).__init__(version=datasets.Version("0.1.2"), **kwargs)
-        self.features = features
-        self.supervised_keys = supervised_keys
-        self.task_templates = task_templates
-class pianos(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("0.1.2")
-    BUILDER_CONFIGS = [
-        pianos_Config(
-            name="eval",
-            features=datasets.Features(
-                {
-                    "mel": datasets.Image(),
-                    "label": datasets.features.ClassLabel(names=_NAMES),
-                    "pitch": datasets.features.ClassLabel(
-                        names=list(_PITCHES.values())
-                    ),
-                }
-            ),
-            supervised_keys=("mel", "label"),
-            task_templates=[
-                ImageClassification(
-                    task="image-classification",
-                    image_column="mel",
-                    label_column="label",
-                )
-            ],
-        ),
-        pianos_Config(
-            name="default",
-            features=datasets.Features(
-                {
-                    "audio": datasets.Audio(sampling_rate=22050),
-                    "mel": datasets.Image(),
-                    "label": datasets.features.ClassLabel(names=_NAMES),
-                    "pitch": datasets.features.ClassLabel(
-                        names=list(_PITCHES.values())
-                    ),
-                }
-            ),
-            supervised_keys=("audio", "label"),
-            task_templates=[
-                AudioClassification(
-                    task="audio-classification",
-                    audio_column="audio",
-                    label_column="label",
-                )
-            ],
-        ),
-    ]
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=self.config.features,
-            homepage=_HOMEPAGE,
-            license="mit",
-            citation=_CITATION,
-            supervised_keys=self.config.supervised_keys,
-            task_templates=self.config.task_templates,
-        )
-    def _split_generators(self, dl_manager):
-        dataset = []
-        if self.config.name == "eval":
-            data_files = dl_manager.download_and_extract(_URLS["eval"])
-            for path in dl_manager.iter_files([data_files]):
-                fname = os.path.basename(path)
-                if fname.endswith(".jpg"):
-                    dataset.append(
-                        {
-                            "mel": path,
-                            "label": os.path.basename(os.path.dirname(path)),
-                            "pitch": _PITCHES[fname.split("_")[0]],
-                        }
-                    )
-        else:
-            subset = {}
-            audio_files = dl_manager.download_and_extract(_URLS["audio"])
-            for path in dl_manager.iter_files([audio_files]):
-                fname = os.path.basename(path)
-                if fname.endswith(".wav"):
-                    subset[fname.split(".")[0]] = {
-                        "audio": path,
-                        "label": os.path.basename(os.path.dirname(path)),
-                        "pitch": _PITCHES[fname[1:4]],
-                    }
-            mel_files = dl_manager.download_and_extract(_URLS["mel"])
-            for path in dl_manager.iter_files([mel_files]):
-                fname = os.path.basename(path)
-                if fname.endswith(".jpg"):
-                    subset[fname.split(".")[0]]["mel"] = path
-            dataset = list(subset.values())
-        random.shuffle(dataset)
-        count = len(dataset)
-        p80 = int(0.8 * count)
-        p90 = int(0.9 * count)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"files": dataset[:p80]}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"files": dataset[p80:p90]}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"files": dataset[p90:]}
-            ),
-        ]
-    def _generate_examples(self, files):
-        if self.config.name == "eval":
-            for i, path in enumerate(files):
-                yield i, {
-                    "mel": path["mel"],
-                    "label": path["label"],
-                    "pitch": path["pitch"],
-                }
-        else:
-            for i, path in enumerate(files):
-                yield i, {
-                    "audio": path["audio"],
-                    "mel": path["mel"],
-                    "label": path["label"],
-                    "pitch": path["pitch"],
-                }

+import os
+import random
+import datasets
+from datasets.tasks import ImageClassification
+_NAMES = {
+    "PearlRiver": [2.33, 2.53, 2.37, 2.41],
+    "YoungChang": [2.53, 2.63, 2.97, 2.71],
+    "Steinway-T": [3.6, 3.63, 3.67, 3.63],
+    "Hsinghai": [3.4, 3.27, 3.2, 3.29],
+    "Kawai": [3.17, 2.5, 2.93, 2.87],
+    "Steinway": [4.23, 3.67, 4, 3.97],
+    "Kawai-G": [3.37, 2.97, 3.07, 3.14],
+    "Yamaha": [3.23, 3.03, 3.17, 3.14],
+}
+_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"
+_CITATION = """\
+@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}
+}
+"""
+_DESCRIPTION = """\
+Piano-Sound-Quality is a dataset of piano sound. It consists of 8 kinds of pianos including PearlRiver, YoungChang, Steinway-T, Hsinghai, Kawai, Steinway, Kawai-G, Yamaha(recorded by Shaohua Ji with SONY PCM-D100). Data was annotated by students from the China Conservatory of Music (CCMUSIC) in Beijing and collected by Monan Zhou.
+"""
+_PITCHES = {
+    "009": "A2",
+    "010": "A2#/B2b",
+    "011": "B2",
+    "100": "C1",
+    "101": "C1#/D1b",
+    "102": "D1",
+    "103": "D1#/E1b",
+    "104": "E1",
+    "105": "F1",
+    "106": "F1#/G1b",
+    "107": "G1",
+    "108": "G1#/A1b",
+    "109": "A1",
+    "110": "A1#/B1b",
+    "111": "B1",
+    "200": "C",
+    "201": "C#/Db",
+    "202": "D",
+    "203": "D#/Eb",
+    "204": "E",
+    "205": "F",
+    "206": "F#/Gb",
+    "207": "G",
+    "208": "G#/Ab",
+    "209": "A",
+    "210": "A#/Bb",
+    "211": "B",
+    "300": "c",
+    "301": "c#/db",
+    "302": "d",
+    "303": "d#/eb",
+    "304": "e",
+    "305": "f",
+    "306": "f#/gb",
+    "307": "g",
+    "308": "g#/ab",
+    "309": "a",
+    "310": "a#/bb",
+    "311": "b",
+    "400": "c1",
+    "401": "c1#/d1b",
+    "402": "d1",
+    "403": "d1#/e1b",
+    "404": "e1",
+    "405": "f1",
+    "406": "f1#/g1b",
+    "407": "g1",
+    "408": "g1#/a1b",
+    "409": "a1",
+    "410": "a1#/b1b",
+    "411": "b1",
+    "500": "c2",
+    "501": "c2#/d2b",
+    "502": "d2",
+    "503": "d2#/e2b",
+    "504": "e2",
+    "505": "f2",
+    "506": "f2#/g2b",
+    "507": "g2",
+    "508": "g2#/a2b",
+    "509": "a2",
+    "510": "a2#/b2b",
+    "511": "b2",
+    "600": "c3",
+    "601": "c3#/d3b",
+    "602": "d3",
+    "603": "d3#/e3b",
+    "604": "e3",
+    "605": "f3",
+    "606": "f3#/g3b",
+    "607": "g3",
+    "608": "g3#/a3b",
+    "609": "a3",
+    "610": "a3#/b3b",
+    "611": "b3",
+    "700": "c4",
+    "701": "c4#/d4b",
+    "702": "d4",
+    "703": "d4#/e4b",
+    "704": "e4",
+    "705": "f4",
+    "706": "f4#/g4b",
+    "707": "g4",
+    "708": "g4#/a4b",
+    "709": "a4",
+    "710": "a4#/b4b",
+    "711": "b4",
+    "800": "c5",
+}
+_URLS = {
+    "audio": f"{_DOMAIN}/audio.zip",
+    "mel": f"{_DOMAIN}/mel.zip",
+    "eval": f"{_DOMAIN}/eval.zip",
+}
+class pianos(datasets.GeneratorBasedBuilder):
+    # BUILDER_CONFIGS = [
+    #     datasets.BuilderConfig(name="default"),
+    #     datasets.BuilderConfig(name="eval"),
+    # ]
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=(
+                datasets.Features(
+                    {
+                        "audio": datasets.Audio(sampling_rate=22050),
+                        "mel": datasets.Image(),
+                        "label": datasets.features.ClassLabel(
+                            names=list(_NAMES.keys())
+                        ),
+                        "pitch": datasets.features.ClassLabel(
+                            names=list(_PITCHES.values())
+                        ),
+                        "bass_score": datasets.Value("float32"),
+                        "mid_score": datasets.Value("float32"),
+                        "treble_score": datasets.Value("float32"),
+                        "avg_score": datasets.Value("float32"),
+                    }
+                )
+                if self.config.name == "default"
+                else datasets.Features(
+                    {
+                        "mel": datasets.Image(),
+                        "label": datasets.features.ClassLabel(
+                            names=list(_NAMES.keys())
+                        ),
+                        "pitch": datasets.features.ClassLabel(
+                            names=list(_PITCHES.values())
+                        ),
+                        "bass_score": datasets.Value("float32"),
+                        "mid_score": datasets.Value("float32"),
+                        "treble_score": datasets.Value("float32"),
+                        "avg_score": datasets.Value("float32"),
+                    }
+                )
+            ),
+            homepage=_HOMEPAGE,
+            license="CC-BY-NC-ND",
+            version="1.2.0",
+            citation=_CITATION,
+            supervised_keys=("mel", "label"),
+            task_templates=ImageClassification(
+                image_column="mel",
+                label_column="label",
+            ),
+        )
+    def _split_generators(self, dl_manager):
+        dataset = []
+        if self.config.name == "default":
+            subset = {}
+            audio_files = dl_manager.download_and_extract(_URLS["audio"])
+            for path in dl_manager.iter_files([audio_files]):
+                fname = os.path.basename(path)
+                if fname.endswith(".wav"):
+                    lebal = os.path.basename(os.path.dirname(path))
+                    subset[fname.split(".")[0]] = {
+                        "audio": path,
+                        "label": lebal,
+                        "pitch": _PITCHES[fname[1:4]],
+                        "bass_score": _NAMES[lebal][0],
+                        "mid_score": _NAMES[lebal][1],
+                        "treble_score": _NAMES[lebal][2],
+                        "avg_score": _NAMES[lebal][3],
+                    }
+            mel_files = dl_manager.download_and_extract(_URLS["mel"])
+            for path in dl_manager.iter_files([mel_files]):
+                fname = os.path.basename(path)
+                if fname.endswith(".jpg"):
+                    subset[fname.split(".")[0]]["mel"] = path
+            dataset = list(subset.values())
+        else:
+            data_files = dl_manager.download_and_extract(_URLS["eval"])
+            for path in dl_manager.iter_files([data_files]):
+                fname: str = os.path.basename(path)
+                if fname.endswith(".jpg"):
+                    lebal = os.path.basename(os.path.dirname(path))
+                    dataset.append(
+                        {
+                            "mel": path,
+                            "label": lebal,
+                            "pitch": _PITCHES[fname.split("_")[0]],
+                            "bass_score": _NAMES[lebal][0],
+                            "mid_score": _NAMES[lebal][1],
+                            "treble_score": _NAMES[lebal][2],
+                            "avg_score": _NAMES[lebal][3],
+                        }
+                    )
+        random.shuffle(dataset)
+        count = len(dataset)
+        p80 = int(0.8 * count)
+        p90 = int(0.9 * count)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"files": dataset[:p80]}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"files": dataset[p80:p90]}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"files": dataset[p90:]}
+            ),
+        ]
+    def _generate_examples(self, files):
+        for i, path in enumerate(files):
+            yield i, path