fixes

hy_asr_grqaser.py

@@ -1,51 +1,53 @@
 ##
 import os
 import pandas as pd
-from datasets import Dataset, DatasetDict, Features, Value, Audio
-
-##
-def load_dataset_script(data_dir):
-    print(f"data_dir: {data_dir}")
-    """
-    Load dataset script for custom audio-transcription dataset.
-
-    :param data_dir: Directory where the data and metadata.csv are stored.
-    :return: A Hugging Face Dataset object.
-    """
-    # Load metadata.csv
-    metadata = pd.read_csv(os.path.join(data_dir, "metadata.csv"))
-    
-    # Create lists for audio files and transcriptions
-    audio_files = []
-    transcriptions = []
-
-    # Iterate through the metadata and populate the lists
-    for _, row in metadata.iterrows():
-        audio_files.append({'path': os.path.join(data_dir, row['file_name'])})
-        transcriptions.append(row['transcription'])
-
-    # Define features of the dataset
-    features = Features({
-        'audio': Audio(sampling_rate=16_000),  # Adjust the sampling rate as needed
-        'sentence': Value('string')
-    })
-
-    # Create a dataset
-    dataset = Dataset.from_dict({
-        'audio': audio_files,
-        'sentence': transcriptions
-    }, features=features)
-
-    # You can split the dataset here if needed, or return as a single dataset
-    return DatasetDict({'train': dataset})
-
-
-## Example usage
+from datasets import DatasetBuilder, DatasetInfo, SplitGenerator, Split, Features, Value, Audio, Version
+
+class HyAsrGrqaser(DatasetBuilder):
+    """Armenian Audio-Transcription Dataset"""
+
+    VERSION = Version("1.0.0")
+
+    def _info(self):
+        return DatasetInfo(
+            description="This dataset contains Armenian speech and transcriptions.",
+            features=Features({
+                'audio': Audio(sampling_rate=16_000),  # Adjust the sampling rate as needed
+                'sentence': Value('string')
+            }),
+            supervised_keys=("audio", "sentence"),
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # Assuming the script is in the root of the project structure
+        data_dir = os.path.dirname(__file__)
+        metadata_path = os.path.join(os.path.dirname(__file__), "metadata.csv")
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir, "metadata_path": metadata_path}
+            ),
+        ]
+
+    def _generate_examples(self, data_dir, metadata_path):
+        """Yields examples."""
+        # Load metadata.csv
+        metadata = pd.read_csv(metadata_path)
+
+        # Generate examples
+        for idx, row in metadata.iterrows():
+            file_path = os.path.join(data_dir, row['file_name'])
+            transcription_path = os.path.join(data_dir, row['transcription_file'])
+            with open(transcription_path, 'r') as f:
+                transcription = f.read().strip()
+            yield idx, {
+                'audio': {'path': file_path},
+                'sentence': transcription
+            }
+
+# Testing the dataset locally
 # if __name__ == "__main__":
-#     data_directory = "C:\\Projects\\aeneas\\hy_asr_grqaser"
-#     dataset = load_dataset_script(data_directory)
-#     print(dataset["train"][1])
-    # from datasets import load_dataset
-    # load_dataset("aburnazy/hy_asr_grqaser", data_dir=".")
-
-
+#     from datasets import load_dataset
+#     dataset = load_dataset("C:\\Projects\\aeneas\\hy_asr_grqaser\\hy_asr_grqaser.py")
+#     print(dataset["train"][0])