Update magnatagatune.py

magnatagatune.py

@@ -1,6 +1,6 @@
 # coding=utf-8
 
-"""MagnaTagATune dataset."""
+"""Medley-Solos-DB dataset."""
 
 
 import os
@@ -24,7 +24,7 @@ logger = logging.getLogger(__name__)
 logger.addHandler(RichHandler())
 logger.setLevel(logging.INFO)
 
-SAMPLE_RATE = 16_000
+SAMPLE_RATE = 44_100
 
 # Cache location
 VERSION = "0.0.1"
@@ -35,43 +35,39 @@ HF_CACHE_HOME = os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))
 DEFAULT_HF_DATASETS_CACHE = os.path.join(HF_CACHE_HOME, "datasets")
 HF_DATASETS_CACHE = Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))
 
-TOP_50_CLASSES = [
-    'ambient', 'beat', 'beats', 'cello', 'choir', 'choral', 'classic', 'classical', 'country', 'dance', 
-    'drums', 'electronic', 'fast', 'female', 'female vocal', 'female voice', 'flute',  'guitar', 'harp', 'harpsichord', 
-    'indian', 'loud', 'male', 'male vocal', 'male voice', 'man',  'metal', 'new age', 'no vocal', 'no vocals', 
-    'no voice', 'opera', 'piano', 'pop', 'quiet',  'rock', 'singing', 'sitar', 'slow', 'soft', 
-    'solo', 'strings', 'synth', 'techno', 'violin',  'vocal', 'vocals', 'voice', 'weird', 'woman'
+CLASSES = [
+    'clarinet', 'distorted electric guitar', 'female singer', 'flute', 'piano', 'tenor saxophone', 'trumpet', 'violin'
 ]
-CLASS2INDEX = {cls:idx for idx, cls in enumerate(TOP_50_CLASSES)}
-INDEX2CLASS = {idx:cls for idx, cls in enumerate(TOP_50_CLASSES)}
+CLASS2INDEX = {cls:idx for idx, cls in enumerate(CLASSES)}
+INDEX2CLASS = {idx:cls for idx, cls in enumerate(CLASSES)}
 
 
-class MagnaTagATuneConfig(datasets.BuilderConfig):
-    """BuilderConfig for MagnaTagATune."""
+class MedleySolosDBConfig(datasets.BuilderConfig):
+    """BuilderConfig for Medley-Solos-DB."""
     
     def __init__(self, features, **kwargs):
-        super(MagnaTagATuneConfig, self).__init__(version=datasets.Version(VERSION, ""), **kwargs)
+        super(MedleySolosDBConfig, self).__init__(version=datasets.Version(VERSION, ""), **kwargs)
         self.features = features
 
 
-class MagnaTagATune(datasets.GeneratorBasedBuilder):
+class MedleySolosDB(datasets.GeneratorBasedBuilder):
 
     BUILDER_CONFIGS = [
-        MagnaTagATuneConfig(
+        MedleySolosDBConfig(
             features=datasets.Features(
                 {
                     "file": datasets.Value("string"),
                     "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
-                    "tags": datasets.Sequence(datasets.Value("string")), 
-                    "label": datasets.Sequence(datasets.features.ClassLabel(names=TOP_50_CLASSES)), 
+                    "instrument": datasets.Value("string"), 
+                    "label": datasets.features.ClassLabel(names=CLASSES), 
                 }
             ),
-            name="top50", 
+            name="v1.2", 
             description="",
         ), 
     ]
 
-    DEFAULT_CONFIG_NAME = "top50"
+    DEFAULT_CONFIG_NAME = "v1.2"
 
     def _info(self):
         return datasets.DatasetInfo(
@@ -83,65 +79,18 @@ class MagnaTagATune(datasets.GeneratorBasedBuilder):
             task_templates=None,
         )
 
-    def _load_metadata(self):
-        # Read metadata
-        df = pd.read_csv("https://mirg.city.ac.uk/datasets/magnatagatune/annotations_final.csv", sep="\t")
-        df = df[df[TOP_50_CLASSES].sum(axis=1) > 0]
-        df = df[TOP_50_CLASSES + ["mp3_path", "clip_id"]]
-        
-        train_ids_df = pd.read_csv(
-            'https://raw.githubusercontent.com/jordipons/musicnn-training/master/data/index/mtt/train_gt_mtt.tsv', 
-            sep='\t', header=None
-        )
-        train_ids = train_ids_df[0].tolist()
-        train_df = df[df["clip_id"].isin(train_ids)]
-
-        validation_ids_df = pd.read_csv(
-            "https://raw.githubusercontent.com/jordipons/musicnn-training/master/data/index/mtt/val_gt_mtt.tsv", 
-            sep="\t", header=None
-        )
-        validation_ids = validation_ids_df[0].tolist()
-        validation_df = df[df["clip_id"].isin(validation_ids)]
-
-        test_ids_df = pd.read_csv(
-            "https://raw.githubusercontent.com/jordipons/musicnn-training/master/data/index/mtt/test_gt_mtt.tsv", 
-            sep="\t", header=None
-        )
-        test_ids = test_ids_df[0].tolist()
-        test_df = df[df["clip_id"].isin(test_ids)]
-        
-        label_names = df.columns
-        label_names = label_names.drop(["mp3_path", "clip_id"])
-
-        return train_df, validation_df, test_df, label_names
-
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        if self.config.name == 'top50':
-            mp3_zip_files = [
-                'https://mirg.city.ac.uk/datasets/magnatagatune/mp3.zip.001', 
-                'https://mirg.city.ac.uk/datasets/magnatagatune/mp3.zip.002', 
-                'https://mirg.city.ac.uk/datasets/magnatagatune/mp3.zip.003', 
-            ]
-            for zip_file_url in mp3_zip_files:
-                _filename = zip_file_url.split('/')[-1]
-                _save_path = os.path.join(
-                    HF_DATASETS_CACHE, 'confit___magnatagatune/top50', VERSION, _filename
-                )
-                download_file(zip_file_url, _save_path)
-                logger.info(f"`{_filename}` is downloaded to {_save_path}")
-
-            main_zip_filename = 'mp3.zip'
-            _save_dir = os.path.join(HF_DATASETS_CACHE, 'confit___magnatagatune/top50', VERSION)
-            _output_file = os.path.join(_save_dir, main_zip_filename)
-            
-            if not os.path.exists(_output_file):
-                logger.info(f"Concatenate zip files to {main_zip_filename}")
-                os.system(f"cat {os.path.join(_save_dir, 'mp3.zip.*')} > {_output_file}")
-
-            archive_path = dl_manager.extract(_output_file)
-            logger.info(f"`{main_zip_filename}` is now extracted to {archive_path}")
-        
+        zip_file_url = "https://zenodo.org/records/3464194/files/Medley-solos-DB.tar.gz"
+        _filename = zip_file_url.split('/')[-1]
+        _save_path = os.path.join(
+            HF_DATASETS_CACHE, 'confit___medley-solos-db/v1.2', VERSION, _filename
+        )
+        download_file(zip_file_url, _save_path)
+        logger.info(f"`{_filename}` is downloaded to {_save_path}")
+        archive_path = dl_manager.extract(_save_path)
+        logger.info(f"`{_filename}` is now extracted to {archive_path}")
+
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN, gen_kwargs={"archive_path": archive_path, "split": "train"}
@@ -154,58 +103,47 @@ class MagnaTagATune(datasets.GeneratorBasedBuilder):
             ), 
         ]
 
-    def _generate_examples(self, archive_path, split=None, metadata_df=None):
-        train_df, validation_df, test_df, label_names = self._load_metadata()
-        extensions = ['.mp3']
-        _, _walker = fast_scandir(archive_path, extensions, recursive=True)
+    def _generate_examples(self, archive_path, split=None):
+        metadata_df = pd.read_csv("https://zenodo.org/records/3464194/files/Medley-solos-DB_metadata.csv")
+        train_df = metadata_df[metadata_df["subset"] == "training"].reset_index(drop=True)
+        validation_df = metadata_df[metadata_df["subset"] == "validation"].reset_index(drop=True)
+        test_df = metadata_df[metadata_df["subset"] == "test"].reset_index(drop=True)
 
-        class2index = {cls:idx for idx, cls in enumerate(label_names)}
-        index2class = {idx:cls for idx, cls in enumerate(label_names)}
+        extensions = ['.wav']
+        _, _walker = fast_scandir(archive_path, extensions, recursive=True)
 
         if split == 'train':
             fileid2class = {}
             for idx, row in train_df.iterrows():
-                fileid = row['mp3_path']
-                class_ = row[label_names].tolist()
-                if sum(class_) == 0:
-                    continue
-                class_ = [idx for idx, val in enumerate(class_) if val != 0]
-                class_ = [index2class.get(idx) for idx in class_]
+                fileid = row['uuid4']
+                class_ = row['instrument']
                 fileid2class[fileid] = class_
         elif split == 'validation':
             fileid2class = {}
             for idx, row in validation_df.iterrows():
-                fileid = row['mp3_path']
-                class_ = row[label_names].tolist()
-                if sum(class_) == 0:
-                    continue
-                class_ = [idx for idx, val in enumerate(class_) if val != 0]
-                class_ = [index2class.get(idx) for idx in class_]
+                fileid = row['uuid4']
+                class_ = row['instrument']
                 fileid2class[fileid] = class_
         elif split == 'test':
             fileid2class = {}
             for idx, row in test_df.iterrows():
-                fileid = row['mp3_path']
-                class_ = row[label_names].tolist()
-                if sum(class_) == 0:
-                    continue
-                class_ = [idx for idx, val in enumerate(class_) if val != 0]
-                class_ = [index2class.get(idx) for idx in class_]
+                fileid = row['uuid4']
+                class_ = row['instrument']
                 fileid2class[fileid] = class_
-
+        
+        _walker = [fileid for fileid in _walker if not Path(fileid).name.startswith('._Medley')]
         for guid, audio_path in enumerate(_walker):
-            parent = Path(audio_path).parent.stem
-            filename = Path(audio_path).name
-            fileid = f"{parent}/{filename}"
+            fileid = Path(audio_path).stem
+            fileid = fileid.split('_')[-1]
             if fileid not in fileid2class:
                 continue
-            tags = fileid2class.get(fileid)
+            instrument = fileid2class.get(fileid)
             yield guid, {
                 "id": str(guid), 
                 "file": audio_path, 
                 "audio": audio_path, 
-                "tags": tags, 
-                "label": tags, 
+                "instrument": instrument, 
+                "label": instrument, 
             }