Update medley-solos-db.py

medley-solos-db.py

@@ -4,25 +4,17 @@
 
 
 import os
-import json
 import gzip
 import shutil
 import pathlib
-import logging
-import textwrap
 import datasets
-import itertools
 import typing as tp
 import pandas as pd
 import urllib.request
 from pathlib import Path
-from copy import deepcopy
+from rich import print
 from tqdm.auto import tqdm
-from rich.logging import RichHandler
 
-logger = logging.getLogger(__name__)
-logger.addHandler(RichHandler())
-logger.setLevel(logging.INFO)
 
 SAMPLE_RATE = 44_100
 
@@ -56,7 +48,6 @@ class MedleySolosDB(datasets.GeneratorBasedBuilder):
         MedleySolosDBConfig(
             features=datasets.Features(
                 {
-                    "file": datasets.Value("string"),
                     "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
                     "instrument": datasets.Value("string"), 
                     "label": datasets.features.ClassLabel(names=CLASSES), 
@@ -87,23 +78,10 @@ class MedleySolosDB(datasets.GeneratorBasedBuilder):
             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}")
+        print(f"`{_filename}` is downloaded to {_save_path}")
         archive_path = dl_manager.extract(_save_path)
-        logger.info(f"`{_filename}` is now extracted to {archive_path}")
+        print(f"`{_filename}` is now extracted to {archive_path}")
 
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"archive_path": archive_path, "split": "train"}
-            ), 
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"archive_path": archive_path, "split": "validation"}
-            ), 
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
-            ), 
-        ]
-
-    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)
@@ -112,33 +90,40 @@ class MedleySolosDB(datasets.GeneratorBasedBuilder):
         extensions = ['.wav']
         _, _walker = fast_scandir(archive_path, extensions, recursive=True)
 
-        if split == 'train':
-            fileid2class = {}
-            for idx, row in train_df.iterrows():
-                fileid = row['uuid4']
-                class_ = row['instrument']
-                fileid2class[fileid] = class_
-        elif split == 'validation':
-            fileid2class = {}
-            for idx, row in validation_df.iterrows():
-                fileid = row['uuid4']
-                class_ = row['instrument']
-                fileid2class[fileid] = class_
-        elif split == 'test':
+        def get_fileid2class(df, split=None):
             fileid2class = {}
-            for idx, row in test_df.iterrows():
-                fileid = row['uuid4']
+            for idx, row in df.iterrows():
+                _fileid = row['uuid4']
                 class_ = row['instrument']
+                class_id = row['instrument_id']
+                fileid = f"Medley-solos-DB_{split}-{class_id}_{_fileid}"
                 fileid2class[fileid] = class_
-                
-        for guid, audio_path in enumerate(_walker):
+            return fileid2class
+
+        train_fileid2class = get_fileid2class(train_df, 'training')
+        validation_fileid2class = get_fileid2class(validation_df, 'validation')
+        test_fileid2class = get_fileid2class(test_df, 'test')
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepaths": _walker, "split": "train", "fileid2class": train_fileid2class}
+            ), 
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": _walker, "split": "validation", "fileid2class": validation_fileid2class}
+            ), 
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepaths": _walker, "split": "test", "fileid2class": test_fileid2class}
+            ), 
+        ]
+
+    def _generate_examples(self, filepaths, split=None, fileid2class=None):
+        for guid, audio_path in enumerate(filepaths):
             fileid = Path(audio_path).stem
             if fileid not in fileid2class:
                 continue
             instrument = fileid2class.get(fileid)
             yield guid, {
                 "id": str(guid), 
-                "file": audio_path, 
                 "audio": audio_path, 
                 "instrument": instrument, 
                 "label": instrument,