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Datasets:
ccmusic-database
/
pianos

Tasks:
Audio Classification
Image Classification
Modalities:
Audio
Image
Formats:
arrow
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
music
art
Libraries:
Datasets
License:
Dataset card Data Studio Files
admin commited on
Commit
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Browse files
Files changed (32) hide show
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README.md CHANGED
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11
  pretty_name: Piano Sound Quality Dataset
12
  size_categories:
13
  - 10K<n<100K
14
- viewer: false
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
15
  ---
16
 
17
  # Dataset Card for Piano Sound Quality Dataset
@@ -104,7 +520,7 @@ Based on the aforementioned original dataset, after data processing, we construc
104
  ```python
105
  from datasets import load_dataset
106
 
107
- ds = load_dataset("ccmusic-database/pianos")
108
  for item in ds["train"]:
109
  print(item)
110
 
@@ -119,7 +535,7 @@ for item in ds["test"]:
119
  ```python
120
  from datasets import load_dataset
121
 
122
- ds = load_dataset("ccmusic-database/pianos", name="8_classes")
123
  for item in ds["train"]:
124
  print(item)
125
 
@@ -133,7 +549,7 @@ for item in ds["test"]:
133
  ### Eval Subset
134
  ```python
135
  from datasets import load_dataset
136
-
137
  ds = load_dataset("ccmusic-database/pianos", name="eval")
138
  for item in ds["train"]:
139
  print(item)
@@ -147,7 +563,7 @@ for item in ds["test"]:
147
 
148
  ## Maintenance
149
  ```bash
150
- git clone [email protected]:datasets/ccmusic-database/pianos
151
  cd pianos
152
  ```
153
 
 
11
  pretty_name: Piano Sound Quality Dataset
12
  size_categories:
13
  - 10K<n<100K
14
+ dataset_info:
15
+ - config_name: default
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+ path: default/validation/data-*.arrow
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+ - split: test
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+ path: default/test/data-*.arrow
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+ - config_name: 8_class
416
+ data_files:
417
+ - split: train
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+ path: 8_class/train/data-*.arrow
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+ - split: validation
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+ path: 8_class/validation/data-*.arrow
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+ path: 8_class/test/data-*.arrow
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+ path: eval/validation/data-*.arrow
429
+ - split: test
430
+ path: eval/test/data-*.arrow
431
  ---
432
 
433
  # Dataset Card for Piano Sound Quality Dataset
 
520
  ```python
521
  from datasets import load_dataset
522
 
523
+ ds = load_dataset("ccmusic-database/pianos", name="default")
524
  for item in ds["train"]:
525
  print(item)
526
 
 
535
  ```python
536
  from datasets import load_dataset
537
 
538
+ ds = load_dataset("ccmusic-database/pianos", name="8_class")
539
  for item in ds["train"]:
540
  print(item)
541
 
 
549
  ### Eval Subset
550
  ```python
551
  from datasets import load_dataset
552
+ # 8 classes
553
  ds = load_dataset("ccmusic-database/pianos", name="eval")
554
  for item in ds["train"]:
555
  print(item)
 
563
 
564
  ## Maintenance
565
  ```bash
566
+ GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/pianos
567
  cd pianos
568
  ```
569
 
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pianos.py DELETED
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- import os
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-
18
- _HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{os.path.basename(__file__)[:-3]}"
19
-
20
- _DOMAIN = f"{_HOMEPAGE}/resolve/master/data"
21
-
22
- _PITCHES = {
23
- "009": "A2",
24
- "010": "A2#/B2b",
25
- "011": "B2",
26
- "100": "C1",
27
- "101": "C1#/D1b",
28
- "102": "D1",
29
- "103": "D1#/E1b",
30
- "104": "E1",
31
- "105": "F1",
32
- "106": "F1#/G1b",
33
- "107": "G1",
34
- "108": "G1#/A1b",
35
- "109": "A1",
36
- "110": "A1#/B1b",
37
- "111": "B1",
38
- "200": "C",
39
- "201": "C#/Db",
40
- "202": "D",
41
- "203": "D#/Eb",
42
- "204": "E",
43
- "205": "F",
44
- "206": "F#/Gb",
45
- "207": "G",
46
- "208": "G#/Ab",
47
- "209": "A",
48
- "210": "A#/Bb",
49
- "211": "B",
50
- "300": "c",
51
- "301": "c#/db",
52
- "302": "d",
53
- "303": "d#/eb",
54
- "304": "e",
55
- "305": "f",
56
- "306": "f#/gb",
57
- "307": "g",
58
- "308": "g#/ab",
59
- "309": "a",
60
- "310": "a#/bb",
61
- "311": "b",
62
- "400": "c1",
63
- "401": "c1#/d1b",
64
- "402": "d1",
65
- "403": "d1#/e1b",
66
- "404": "e1",
67
- "405": "f1",
68
- "406": "f1#/g1b",
69
- "407": "g1",
70
- "408": "g1#/a1b",
71
- "409": "a1",
72
- "410": "a1#/b1b",
73
- "411": "b1",
74
- "500": "c2",
75
- "501": "c2#/d2b",
76
- "502": "d2",
77
- "503": "d2#/e2b",
78
- "504": "e2",
79
- "505": "f2",
80
- "506": "f2#/g2b",
81
- "507": "g2",
82
- "508": "g2#/a2b",
83
- "509": "a2",
84
- "510": "a2#/b2b",
85
- "511": "b2",
86
- "600": "c3",
87
- "601": "c3#/d3b",
88
- "602": "d3",
89
- "603": "d3#/e3b",
90
- "604": "e3",
91
- "605": "f3",
92
- "606": "f3#/g3b",
93
- "607": "g3",
94
- "608": "g3#/a3b",
95
- "609": "a3",
96
- "610": "a3#/b3b",
97
- "611": "b3",
98
- "700": "c4",
99
- "701": "c4#/d4b",
100
- "702": "d4",
101
- "703": "d4#/e4b",
102
- "704": "e4",
103
- "705": "f4",
104
- "706": "f4#/g4b",
105
- "707": "g4",
106
- "708": "g4#/a4b",
107
- "709": "a4",
108
- "710": "a4#/b4b",
109
- "711": "b4",
110
- "800": "c5",
111
- }
112
-
113
- _URLS = {
114
- "audio": f"{_DOMAIN}/audio.zip",
115
- "mel": f"{_DOMAIN}/mel.zip",
116
- "eval": f"{_DOMAIN}/eval.zip",
117
- }
118
-
119
-
120
- class pianos(datasets.GeneratorBasedBuilder):
121
- def _info(self):
122
- names = list(_NAMES.keys())
123
- if self.config.name == "default":
124
- names = names[:-1]
125
-
126
- return datasets.DatasetInfo(
127
- features=(
128
- datasets.Features(
129
- {
130
- "audio": datasets.Audio(sampling_rate=44100),
131
- "mel": datasets.Image(),
132
- "label": datasets.features.ClassLabel(names=names),
133
- "pitch": datasets.features.ClassLabel(
134
- names=list(_PITCHES.values())
135
- ),
136
- "bass_score": datasets.Value("float32"),
137
- "mid_score": datasets.Value("float32"),
138
- "treble_score": datasets.Value("float32"),
139
- "avg_score": datasets.Value("float32"),
140
- }
141
- )
142
- if self.config.name != "eval"
143
- else datasets.Features(
144
- {
145
- "mel": datasets.Image(),
146
- "label": datasets.features.ClassLabel(names=names),
147
- "pitch": datasets.features.ClassLabel(
148
- names=list(_PITCHES.values())
149
- ),
150
- "bass_score": datasets.Value("float32"),
151
- "mid_score": datasets.Value("float32"),
152
- "treble_score": datasets.Value("float32"),
153
- "avg_score": datasets.Value("float32"),
154
- }
155
- )
156
- ),
157
- homepage=_HOMEPAGE,
158
- license="CC-BY-NC-ND",
159
- version="1.2.0",
160
- supervised_keys=("mel", "label"),
161
- task_templates=ImageClassification(
162
- image_column="mel",
163
- label_column="label",
164
- ),
165
- )
166
-
167
- def _split_generators(self, dl_manager):
168
- dataset = []
169
- if self.config.name != "eval":
170
- subset = {}
171
- audio_files = dl_manager.download_and_extract(_URLS["audio"])
172
- for path in dl_manager.iter_files([audio_files]):
173
- fname = os.path.basename(path)
174
- if fname.endswith(".wav"):
175
- lebal = os.path.basename(os.path.dirname(path))
176
- if self.config.name == "default" and lebal == "Yamaha":
177
- continue
178
-
179
- subset[fname.split(".")[0]] = {
180
- "audio": path,
181
- "label": lebal,
182
- "pitch": _PITCHES[fname[1:4]],
183
- "bass_score": _NAMES[lebal][0],
184
- "mid_score": _NAMES[lebal][1],
185
- "treble_score": _NAMES[lebal][2],
186
- "avg_score": _NAMES[lebal][3],
187
- }
188
-
189
- mel_files = dl_manager.download_and_extract(_URLS["mel"])
190
- for path in dl_manager.iter_files([mel_files]):
191
- fname = os.path.basename(path)
192
- pname = fname.split(".")[0]
193
- if fname.endswith(".jpg") and pname in subset:
194
- subset[pname]["mel"] = path
195
-
196
- dataset = list(subset.values())
197
-
198
- else:
199
- data_files = dl_manager.download_and_extract(_URLS["eval"])
200
- for path in dl_manager.iter_files([data_files]):
201
- fname: str = os.path.basename(path)
202
- if fname.endswith(".jpg"):
203
- lebal = os.path.basename(os.path.dirname(path))
204
- dataset.append(
205
- {
206
- "mel": path,
207
- "label": lebal,
208
- "pitch": _PITCHES[fname.split("_")[0]],
209
- "bass_score": _NAMES[lebal][0],
210
- "mid_score": _NAMES[lebal][1],
211
- "treble_score": _NAMES[lebal][2],
212
- "avg_score": _NAMES[lebal][3],
213
- }
214
- )
215
-
216
- names = list(_NAMES.keys())
217
- if self.config.name == "default":
218
- names = names[:-1]
219
-
220
- categories = {}
221
- for name in names:
222
- categories[name] = []
223
-
224
- for data in dataset:
225
- categories[data["label"]].append(data)
226
-
227
- testset, validset, trainset = [], [], []
228
- for cls in categories:
229
- random.shuffle(categories[cls])
230
- count = len(categories[cls])
231
- p80 = int(count * 0.8)
232
- p90 = int(count * 0.9)
233
- trainset += categories[cls][:p80]
234
- validset += categories[cls][p80:p90]
235
- testset += categories[cls][p90:]
236
-
237
- random.shuffle(trainset)
238
- random.shuffle(validset)
239
- random.shuffle(testset)
240
- return [
241
- datasets.SplitGenerator(
242
- name=datasets.Split.TRAIN, gen_kwargs={"files": trainset}
243
- ),
244
- datasets.SplitGenerator(
245
- name=datasets.Split.VALIDATION, gen_kwargs={"files": validset}
246
- ),
247
- datasets.SplitGenerator(
248
- name=datasets.Split.TEST, gen_kwargs={"files": testset}
249
- ),
250
- ]
251
-
252
- def _generate_examples(self, files):
253
- for i, path in enumerate(files):
254
- yield i, path