edit files

su_id_asr_split.py

@@ -6,8 +6,11 @@ import datasets
 
 from seacrowd.utils import schemas
 from seacrowd.utils.configs import SEACrowdConfig
-from seacrowd.utils.constants import (DEFAULT_SEACROWD_VIEW_NAME,
-                                       DEFAULT_SOURCE_VIEW_NAME, Tasks)
+from seacrowd.utils.constants import (
+    DEFAULT_SEACROWD_VIEW_NAME,
+    DEFAULT_SOURCE_VIEW_NAME,
+    Tasks,
+)
 
 _DATASETNAME = "su_id_asr"
 _SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
@@ -32,6 +35,7 @@ This dataset was collected by Google in Indonesia.
 """
 
 _HOMEPAGE = "https://indonlp.github.io/nusa-catalogue/card.html?su_id_asr"
+
 _LICENSE = "Attribution-ShareAlike 4.0 International."
 
 _URLs = {
@@ -41,83 +45,102 @@ _URLs = {
 }
 
 _SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
+
 _SOURCE_VERSION = "1.0.0"
 _SEACROWD_VERSION = "2024.06.20"
 
 
 class SuIdASR(datasets.GeneratorBasedBuilder):
+    """su_id contains ~220K utterances for Sundanese ASR training data."""
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="su_id_asr_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="SU_ID_ASR source schema",
+            schema="source",
+            subset_id="su_id_asr",
+        ),
+        SEACrowdConfig(
+            name="su_id_asr_seacrowd_sptext",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description="SU_ID_ASR Nusantara schema",
+            schema="seacrowd_sptext",
+            subset_id="su_id_asr",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "su_id_asr_source"
+
     def _info(self):
-        features = datasets.Features({
-            "id": datasets.Value("string"),
-            "speaker_id": datasets.Value("string"),
-            "audio": datasets.Audio(sampling_rate=16_000),
-            "text": datasets.Value("string"),
-        })
-        return datasets.DatasetInfo(features=features)
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "speaker_id": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "text": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "seacrowd_sptext":
+            features = schemas.speech_text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            task_templates=[datasets.AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
+        )
 
     def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        print("Downloading the dataset...")
-
-        # Download the files
-        train_file = dl_manager.download(_URLs["su_id_asr_train"])
-        dev_file = dl_manager.download(_URLs["su_id_asr_dev"])
-        test_file = dl_manager.download(_URLs["su_id_asr_test"])
+        base_path_train = dl_manager.download_and_extract(_URLs["su_id_asr_train"])
+        base_path_validation = dl_manager.download_and_extract(_URLs["su_id_asr_dev"])
+        base_path_test = dl_manager.download_and_extract(_URLs["su_id_asr_test"])
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": train_file},
+                gen_kwargs={"filepath": base_path_train, "split": "train"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": dev_file},
+                gen_kwargs={"filepath": base_path_validation, "split": "validation"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={"filepath": test_file},
+                gen_kwargs={"filepath": base_path_test, "split": "test"},
             ),
         ]
 
     def _generate_examples(self, filepath: str):
-        """Yields examples as (key, example) tuples."""
-        try:
-            with open(filepath, "r") as f:
-                tsv_reader = csv.reader(f, delimiter="\t")
-                header = next(tsv_reader)  # Skip header if present
-                print(f"Header: {header}")  # Print the header for debugging
-                
-                for line in tsv_reader:
-                    print(f"Line: {line}")  # Print each line to check contents
-                    if len(line) < 3:  # Ensure there are enough columns
-                        print("Skipping line, not enough columns:", line)
-                        continue
-                    
-                    audio_id, sp_id, text = line[0], line[1], line[2]
-                    wav_path = os.path.join(os.path.dirname(filepath), f"{audio_id}.flac")
-
-                    # Check if the audio file exists
-                    if os.path.exists(wav_path):
-                        yield audio_id, {
-                            "id": audio_id,
-                            "speaker_id": sp_id,
-                            "audio": wav_path,
-                            "text": text,
+        # Construct the path for the TSV file
+        tsv_file = os.path.join(filepath, "utt_spk_text.tsv")
+
+        # Check if the TSV file exists
+        if not os.path.exists(tsv_file):
+            raise FileNotFoundError(f"TSV file not found at: {tsv_file}")
+
+        with open(tsv_file, "r") as file:
+            tsv_reader = csv.reader(file, delimiter="\t")
+
+            for line in tsv_reader:
+                audio_id, speaker_id, transcription_text = line[0], line[1], line[2]
+                wav_path = os.path.join(filepath, "{}.flac".format(audio_id))
+
+                if os.path.exists(wav_path):
+                    ex = {
+                        "id": audio_id,
+                        "speaker_id": speaker_id,
+                        "path": wav_path,
+                        "audio": wav_path,
+                        "text": transcription_text,
+                    }
+                    if self.config.schema == "seacrowd_sptext":
+                        ex["metadata"] = {
+                            "speaker_age": None,
+                            "speaker_gender": None,
                         }
-                    else:
-                        print(f"Audio file does not exist: {wav_path}. Skipping this entry.")
-
-            print(f"Completed loading data from {filepath}.")
-
-        except FileNotFoundError:
-            print(f"Error: The file {filepath} was not found. Please check the path.")
-        except Exception as e:
-            print(f"An error occurred: {e}")
-
-# Load the dataset
-try:
-    dataset = datasets.load_dataset(__name__)  # Use the current module name
-    print("Dataset loaded successfully.")
-    print(dataset)
-
-except Exception as e:
-    print(f"An error occurred while loading the dataset: {e}")
+                    yield audio_id, ex