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script/process_audio.py

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+from mutagen.flac import FLAC
+from mutagen.mp3 import MP3
+
+import io
+import ast
+
+
+
+def _get_audio_duration(bytes, file_type):
+    """
+    Get the flac file duration. 
+    """
+
+    # Load the byte data into a BytesIO object
+    data = io.BytesIO(bytes)
+
+    if file_type == "flac":
+        # Load the bytes data
+        audio = FLAC(data)
+    if file_type == "mp3":
+        audio = MP3(data)
+    
+    # Get the duration in seconds
+    duration = audio.info.length
+
+    return str(duration)
+# ---
+# Have to make two seperate functions to process audio string, 
+# in order to handle the dask partition properly. 
+def process_audio_string_path(audio_str):
+    audio_dict = ast.literal_eval(audio_str)
+    return audio_dict["path"]
+
+def process_audio_string_duration(audio_str):
+    audio_dict = ast.literal_eval(audio_str)
+    path = audio_dict["path"]
+    a_type = path.split(".")[1]
+    try:
+        duration = _get_audio_duration(audio_dict["bytes"], a_type)
+        return duration
+    except Exception as e:
+        print(f"Get error {e}")
+        return "n/a"
+
+def process_audio_partition(partition):
+    """
+    Process the audio column from the dataframe to get the path and duration.
+    
+    """
+    # Perform operations on each partition as if it were a Pandas DataFrame
+    partition['path'] = partition['audio'].apply(process_audio_string_path)
+    partition['duration'] = partition['audio'].apply(process_audio_string_duration)
+    return partition
+
+
+def process_audio_column(result_df):
+
+    # Extra steps to hanlde audio column.
+    meta = result_df.head(0)  # Use the structure of the original DataFrame and add the new column
+    meta['path'] = 'string'  
+    meta['duration'] = 'string'
+    
+    result_df = result_df.map_partitions(process_audio_partition, meta=meta)
+    result_df = result_df.drop(columns=['audio'])
+
+    return result_df