change to img dataset

README.md

@@ -48,8 +48,35 @@ Piano Sound Classification, pitch detection
 English
 
 ## Dataset Structure
+<style>
+  #pianos td {
+    vertical-align: middle !important;
+    text-align: center;
+  }
+  #pianos th {
+    text-align: center;
+  }
+</style>
+<table id="pianos">
+    <tr>
+        <th>mel(.jpg)</th>
+        <th>label</th>
+        <th>pitch</th>
+    </tr>
+    <tr>
+        <td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/W8wy7pkYZtCt3lI5Oq39l.jpeg"></td>
+        <td>PearlRiver, YoungChang, Steinway-T, Hsinghai, Kawai, Steinway, Kawai-G, Yamaha</td>
+        <td>88 pitches on piano</td>
+    </tr>
+    <tr>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+    </tr>
+</table>
+
 ### Data Instances
-.zip(.wav)
+.zip(.wav, jpg)
 
 ### Data Fields
 ```
@@ -94,7 +121,7 @@ Help developing piano sound quality rating apps
 Only for pianos
 
 ### Other Known Limitations
-No black key in Steinway
+No black key in Steinway, data imbalance
 
 ## Additional Information
 ### Dataset Curators

data/Kawai.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:40c7f45e0210078c722d64a532b8765d6646571483a4ee883c944ffc57aed0cd
-size 38260273

data/PearlRiver.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:df04b3e1cd2510012f23bbbe0c38ce2e90b52409da9a1621736c9a3820527e67
-size 16001270

data/Steinway-T.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7984f7ac676c7f787e8604553c5742f0d89c526b647af3468b93895c33cf5714
-size 61769515

data/Steinway.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f9acf77cffdbcb2fd72bb24bff406122d738df2cb5d5eb5f9b3205af7a4f1833
-size 35046108

data/Yamaha.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e0237300cbdf91787f8089e435566ed8037891ad3c579afa5d7c3fbbe4821643
-size 46875289

data/YoungChang.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b50a226129fa6d4dfceb4284cc1f5a74df657b7fb809d57624e14194d9cd55f0
-size 99240372

data/{Hsinghai.zip → pianos_data.zip}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4c91755e2f2a129d7a5a4ab3976c56729b07fc0f06aedc0cd386b187ae0b63d7
-size 35420765
+oid sha256:dfa2d10eb0be43addb9ba7245d667a3f33095adee268ba70545a99f8cee3ef39
+size 392112086

data/{Kawai-G.zip → pianos_rawdata.zip}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2cb556cf675877536c4b695bdaa5c483a4b80e518de3dd8a602fc0033ef53e69
-size 19742026
+oid sha256:962f4f5e1c94e721155043f86234f5e133ac0cd16b4e98eb53f2112c6d1b2fc1
+size 352375886

pianos.py

@@ -1,14 +1,10 @@
-import io
 import os
-import wave
 import random
-import zipfile
+import socket
 import datasets
-import requests
-from datasets.tasks import AudioClassification
+from datasets.tasks import ImageClassification
 
 
-# Once upload a new piano brand zip, please register its name here
 _NAMES = [
     "PearlRiver",
     "YoungChang",
@@ -20,7 +16,9 @@ _NAMES = [
     "Yamaha",
 ]
 
-_HOMEPAGE = f"https://huggingface.co/datasets/ccmusic-database/{os.path.basename(__file__).split('.')[0]}"
+_NAME = os.path.basename(__file__).split('.')[0]
+
+_HOMEPAGE = f"https://huggingface.co/datasets/ccmusic-database/{_NAME}"
 
 _CITATION = """\
 @dataset{zhaorui_liu_2021_5676893,
@@ -36,29 +34,25 @@ _CITATION = """\
 """
 
 _DESCRIPTION = """\
-Piano-Sound-Quality-Database 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 George Chou.
+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.
 """
 
-_URLS = {piano: f"{_HOMEPAGE}/resolve/main/data/{piano}.zip" for piano in _NAMES}
-
-
-_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"}
+_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"
+}
 
 
 class pianos(datasets.GeneratorBasedBuilder):
@@ -67,46 +61,45 @@ class pianos(datasets.GeneratorBasedBuilder):
             description=_DESCRIPTION,
             features=datasets.Features(
                 {
-                    "audio": datasets.Audio(sampling_rate=44_100),
+                    "mel": datasets.Image(),
                     "label": datasets.features.ClassLabel(names=_NAMES),
                     "pitch": datasets.features.ClassLabel(names=list(_PITCHES.values())),
                 }
             ),
-            supervised_keys=("audio", "label"),
+            supervised_keys=("mel", "label"),
             homepage=_HOMEPAGE,
             license="mit",
             citation=_CITATION,
             task_templates=[
-                AudioClassification(
-                    task="audio-classification",
-                    audio_column="audio",
+                ImageClassification(
+                    task="image-classification",
+                    image_column="mel",
                     label_column="label",
                 )
             ],
         )
 
-    def _get_wav_dur_from_byte(self, file_bytes):
-        with wave.open(io.BytesIO(file_bytes), 'r') as wav_file:
-            frames = wav_file.getnframes()
-            rate = wav_file.getframerate()
-            duration = frames / float(rate)
-        return round(duration, 3)
+    def _cdn_url(self, ip='127.0.0.1', port=80):
+        try:
+            # easy for local test
+            with socket.create_connection((ip, port), timeout=5):
+                return f'http://{ip}/{_NAME}/data/{_NAME}_data.zip'
 
-    def _get_wav_dur_in_zip(self, zip_url, wav_file_path):
-        resp = requests.get(zip_url)
-        with zipfile.ZipFile(io.BytesIO(resp.content)) as zip_file:
-            with zip_file.open(wav_file_path) as file:
-                file_data = file.read()
-                return self._get_wav_dur_from_byte(file_data)
+        except (socket.timeout, socket.error):
+            return f"{_HOMEPAGE}/resolve/main/data/{_NAME}_data.zip",
 
     def _split_generators(self, dl_manager):
-        data_files = dl_manager.download_and_extract(_URLS)
+        data_files = dl_manager.download_and_extract(self._cdn_url())
         dataset = []
 
-        for index in _URLS.keys():
-            for path in dl_manager.iter_files([data_files[index]]):
-                if os.path.basename(path).endswith(".wav"):
-                    dataset.append(path)
+        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]]
+                })
 
         random.shuffle(dataset)
         count = len(dataset)
@@ -117,27 +110,27 @@ class pianos(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "files": dataset[:p80],
-                },
+                    "files": dataset[:p80]
+                }
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "files": dataset[p80:p90],
-                },
+                    "files": dataset[p80:p90]
+                }
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "files": dataset[p90:],
-                },
+                    "files": dataset[p90:]
+                }
             ),
         ]
 
     def _generate_examples(self, files):
         for i, path in enumerate(files):
             yield i, {
-                "audio": path,
-                "label": os.path.basename(os.path.dirname(path)),
-                "pitch": _PITCHES[os.path.basename(path)[1:4]],
+                "mel": path['mel'],
+                "label": path['label'],
+                "pitch": path['pitch']
             }