metadata_se_csv.zip

archive/nst/metadata_se_csv.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:94993f25e3cc9415a465dbdb1529a300aca3dc24e0a29cdfb112d153be06f1bc
+size 19735371

babelbox_voice.py

@@ -29,7 +29,7 @@ _DL_URLS = [
     "https://huggingface.co/datasets/babelbox/babelbox_voice/resolve/main/archive/nst/nst-data-002.tar.gz"
 ]
 
-_METADATA_URL = "https://huggingface.co/datasets/babelbox/babelbox_voice/resolve/main/archive/nst/metadata_se_csv.zip"
+_METADATA_URL = "https://huggingface.co/datasets/babelbox/babelbox_voice/resolve/main/archive/nst/metadata.tar.gz"
 
 class BabelboxVoiceConfig(datasets.BuilderConfig):
     """BuilderConfig for BabelboxVoice."""
@@ -95,12 +95,24 @@ class BabelboxVoice(datasets.GeneratorBasedBuilder):
         
         local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
         
-        meta_url = self.config.meta_url
+        #meta_url = self.config.meta_url
         
-        meta_path = dl_manager.download_and_extract(_METADATA_URL)
+        #meta_path = dl_manager.download_and_extract(_METADATA_URL)
         
-        print(meta_path, Path(meta_path))
+        meta_archive = dl_manager.iter_archive(_METADATA_URL)
         
+        print(meta_archive)
+        
+        metadata = {}
+        for path, file in meta_archive:
+            print(type(file))
+            reader = csv.DictReader(file)
+            for row in tqdm(reader, desc="Reading metadata..."):
+                filename = row['filename_channel_1']
+                sentence = row['text']
+                metadata[filename] = sentence
+            
+        """
         metadata = {}
         with open(meta_path, encoding="utf-8") as f:
             reader = csv.DictReader(f)
@@ -108,6 +120,7 @@ class BabelboxVoice(datasets.GeneratorBasedBuilder):
                 filename = row['filename_channel_1']
                 sentence = row['text']
                 metadata[filename] = sentence
+        """
         
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN,