range(1,31) fix

babelbox_voice.py

@@ -83,7 +83,7 @@ class BabelboxVoice(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
         
-        _DL_URLS = [_DL_URL_FORMAT.format(i) for i in range(30) ]
+        _DL_URLS = [_DL_URL_FORMAT.format(i) for i in range(1,31) ]
             
         archive_paths = dl_manager.download(_DL_URLS)
         local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
@@ -99,34 +99,7 @@ class BabelboxVoice(datasets.GeneratorBasedBuilder):
                         filename = row['filename_channel_1']
                         sentence = row['text']
                         metadata[filename] = sentence
-                        
-            
-        """
-        metadata = {}
-        if dl_manager.is_streaming:
-            meta_archive = dl_manager.iter_archive(_METADATA_URL)
-            for path, file in meta_archive:
-                reader = csv.DictReader(codecs.iterdecode(file, 'utf-8'))
-                for row in tqdm(reader, desc="Reading metadata..."):
-                    filename = row['filename_channel_1']
-                    sentence = row['text']
-                    metadata[filename] = sentence
-        else:
-            metadata_path = dl_manager.download(_METADATA_URL)
-            local_extracted_metadata_path = dl_manager.extract(metadata_path)
-            metadata_path = Path(local_extracted_metadata_path)
-            for filename in metadata_path.glob("*.csv"):
-                with open(filename) as csv_file:
-                    reader = csv.DictReader(csv_file)
-                    for row in tqdm(reader, desc="Reading metadata..."):
-                        filename = row['filename_channel_1']
-                        sentence = row['text']
-                        metadata[filename] = sentence
-         
-         """
-            
-            
-        
+                                
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN, 
                 gen_kwargs={