feat: split train, val, test

su_id_asr.py

@@ -27,8 +27,7 @@ _CITATION = """\
 """
 
 _DESCRIPTION = """\
-Sundanese ASR training data set containing ~220K utterances.
-This dataset was collected by Google in Indonesia.
+Test
 """
 
 _HOMEPAGE = "https://indonlp.github.io/nusa-catalogue/card.html?su_id_asr"
@@ -44,6 +43,7 @@ _SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
 _SOURCE_VERSION = "1.0.0"
 _SEACROWD_VERSION = "2024.06.20"
 
+GROUP_DATASET_ID = 6
 
 class SuIdASR(datasets.GeneratorBasedBuilder):
     """su_id contains ~220K utterances for Sundanese ASR training data."""
@@ -92,29 +92,35 @@ class SuIdASR(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
         base_path = {}
-        for id in range(10):
-            base_path[id] = dl_manager.download_and_extract(_URLs["su_id_asr"].format(str(id)))
-        for id in ["a", "b", "c", "d", "e", "f"]:
-            base_path[id] = dl_manager.download_and_extract(_URLs["su_id_asr"].format(str(id)))
+        base_path[id] = dl_manager.download_and_extract(_URLs["su_id_asr"].format(str(GROUP_DATASET_ID)))
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": base_path},
+                gen_kwargs={"filepath": f"{base_path}/train"},
+            ),
+            datasets.SplitGenerator(
+            name=datasets.Split.VALIDATION,
+            gen_kwargs={"filepath": f"{base_path}/validation"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": f"{base_path}/test"},
             ),
         ]
 
     def _generate_examples(self, filepath: Dict):
 
         if self.config.schema == "source" or self.config.schema == "seacrowd_sptext":
-
-            for key, each_filepath in filepath.items():
-
+            
+            # Iterate through the dictionary that contains split paths
+            for split, each_filepath in filepath.items():
+                
                 tsv_file = os.path.join(each_filepath, "asr_sundanese", "utt_spk_text.tsv")
 
                 with open(tsv_file, "r") as file:
-                    tsv_file = csv.reader(file, delimiter="\t")
+                    tsv_reader = csv.reader(file, delimiter="\t")
 
-                    for line in tsv_file:
+                    for line in tsv_reader:
                         audio_id, speaker_id, transcription_text = line[0], line[1], line[2]
 
                         wav_path = os.path.join(each_filepath, "asr_sundanese", "data", "{}".format(audio_id[:2]), "{}.flac".format(audio_id))