Create pianos.py

pianos.py

@@ -0,0 +1,125 @@
+# coding=utf-8
+
+"""Pianos pitch classification dataset."""
+
+
+import os
+import random
+import textwrap
+import datasets
+import itertools
+import typing as tp
+from pathlib import Path
+
+from ._pianos import _PITCHES
+
+_COMPRESSED_FILENAME = 'pianos.zip'
+
+_NAMES = [
+    "PearlRiver",
+    "YoungChang",
+    "Steinway-T",
+    "Hsinghai",
+    "Kawai",
+    "Steinway",
+    "Kawai-G",
+    "Yamaha",
+]
+
+SAMPLE_RATE = 16_000
+
+_CITATION = """\
+@dataset{zhaorui_liu_2021_5676893,
+  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li},
+  title        = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
+  month        = {nov},
+  year         = {2021},
+  publisher    = {Zenodo},
+  version      = {1.1},
+  doi          = {10.5281/zenodo.5676893},
+  url          = {https://doi.org/10.5281/zenodo.5676893}
+}
+"""
+
+_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.
+"""
+
+
+class PianosConfig(datasets.BuilderConfig):
+    """BuilderConfig for Pianos."""
+    
+    def __init__(self, features, **kwargs):
+        super(PianosConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs)
+        self.features = features
+
+
+class pianos(datasets.GeneratorBasedBuilder):
+    
+    BUILDER_CONFIGS = [
+        MswcConfig(
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
+                    "pitch": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=list(_PITCHES.values()), 
+                }
+            ),
+            name="pitch", 
+            description=textwrap.dedent(_DESCRIPTION), 
+        ), 
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=textwrap.dedent(_DESCRIPTION),
+            features=self.config.features,
+            supervised_keys=None,
+            homepage="",
+            citation=textwrap.dedent(_CITATION),
+            task_templates=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.extract(_COMPRESSED_FILENAME)
+        dataset = []
+
+        for path in dl_manager.iter_files([data_files]):
+            fname = os.path.basename(path)
+            if fname.endswith(".wav"):
+                dataset.append(
+                    {
+                        "file": path, 
+                        "audio": path, 
+                        # "label": os.path.basename(os.path.dirname(path)),
+                        "label": _PITCHES[fname.split("_")[0]],
+                        "pitch": _PITCHES[fname.split("_")[0]],
+                    }
+                )
+
+        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 guid, path in enumerate(files):
+            yield guid, {
+                "id": str(guid), 
+                "audio": path["audio"],
+                "label": path["label"],
+                "pitch": path["pitch"],
+            }