Delete alphanumeric-audio-dataset.py

alphanumeric-audio-dataset.py

@@ -1,75 +0,0 @@
-import os
-import pandas as pd
-from datasets import Dataset, Audio
-
-def load_metadata(metadata_path):
-    """Load the metadata CSV file."""
-    return pd.read_csv(metadata_path)
-
-def generate_dataset_dict(metadata, audio_folder):
-    """
-    Generate the dataset dictionary by mapping metadata to corresponding audio files.
-    """
-    data = {
-        "file_name_id": [],
-        "audio": [],
-        "text": [],
-        "age": [],
-        "gender": [],
-        "nationality": [],
-        "native_language": [],
-        "familiarity_with_english": [],
-        "accent_strength": [],
-        "difficulties": [],
-        "recording_machine": [],
-    }
-    
-    for id in metadata['Response_ID'].unique():
-
-        df_subset = metadata[metadata['Response_ID']==id]
-        
-        # Collect audio paths for different categories
-        name_audio_path = df_subset['file_name'].iloc[0]
-        number_audio_path = df_subset['file_name'].iloc[2]
-        address_audio_path = df_subset['file_name'].iloc[1]
-        
-        row = df_subset.iloc[0]
-
-        # Only include data if all three audio files exist
-        if name_audio_path and number_audio_path and address_audio_path:
-            data["file_name_id"].append(id)
-            data["audio"].append({
-                "name_audio": name_audio_path,
-                "number_audio": number_audio_path,
-                "address_audio": address_audio_path,
-            })
-            data["text"].append({
-                "name": row["Name"],
-                "number": row["Number"],
-                "address": row["Address"],
-            })
-            data["age"].append(row["Age"])
-            data["gender"].append(row["Gender"])
-            data["nationality"].append(row["Nationality"])
-            data["native_language"].append(row["Native Language"])
-            data["familiarity_with_english"].append(row["Familiarity with English"])
-            data["accent_strength"].append(row["Accent Strength (Self reported)"])
-            data["difficulties"].append(row["Difficulties"])
-            data["recording_machine"].append(row["Recording Machine"])
-    
-    return data
-
-def load_dataset(metadata_path="metadata.csv", audio_folder="audio_data"):
-    """
-    Load the dataset, mapping metadata to audio and other fields.
-    """
-    metadata = load_metadata(metadata_path)
-    dataset_dict = generate_dataset_dict(metadata, audio_folder)
-    
-    # Use the Audio feature from the Hugging Face Datasets library
-    return Dataset.from_dict(dataset_dict).cast_column("audio", Audio())
-
-if __name__ == "__main__":
-    # Load the dataset and print a sample
-    dataset = load_dataset()
-    print(dataset)