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digital_green_process_data.py

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+import os
+import pandas as pd
+from datasets import Dataset, DatasetDict, Audio
+import soundfile as sf
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+# Paths
+audio_folder = '/home/azureuser/data2/dg_16/'  # Path where your audio files are stored
+csv_file = 'digital_green_recordings.csv'   # Path to the CSV that contains audio paths and transcripts
+
+# Read your CSV file (assumes it has columns: 'path' and 'transcript')
+df = pd.read_csv(csv_file, sep="$")
+
+# Create a new column for client_id (random or default if you don’t have speaker info)
+df['client_id'] = ['speaker_' + str(i) for i in range(len(df))]
+
+# If your CSV has relative paths, ensure the paths are correct
+df['path'] = df['path'].apply(lambda x: os.path.join(audio_folder, x))
+
+# Add additional columns needed for the Common Voice format (can be optional)
+df['up_votes'] = 0
+df['down_votes'] = 0
+df['age'] = None
+df['gender'] = None
+df['accent'] = None
+
+# Function to load and possibly convert audio to mono
+def load_audio(file_path):
+        # Load audio file
+    audio, sr = sf.read(file_path)
+                # Convert to mono if stereo
+    if len(audio.shape) > 1:
+        audio = np.mean(audio, axis=1)
+        return {'audio': {'array': audio, 'sampling_rate': sr}}
+
+                                # Apply audio loading function to DataFrame
+df['audio'] = df['path'].apply(lambda x: load_audio(x))
+
+train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)  # Adjust test_size as needed
+
+# Convert DataFrames to Hugging Face Datasets
+train_dataset = Dataset.from_pandas(train_df)
+test_dataset = Dataset.from_pandas(test_df)
+
+# Cast the 'audio' column to the 'audio' type
+train_dataset = train_dataset.cast_column('audio', Audio())
+test_dataset = test_dataset.cast_column('audio', Audio())
+
+                        # Create a DatasetDict to simulate train/test/validation splits if needed
+dataset_dict = DatasetDict({
+                        'train': train_dataset,
+                        'test': test_dataset # If you have separate splits, add them here (e.g., 'train', 'test', 'validation')
+                        })
+
+# Save the dataset (optional) for future use
+dataset_dict.save_to_disk('data2/digital_green_data')
+
+                        # Print a sample from the dataset
+print(dataset_dict['train'][0])
+
+print(dataset_dict['test'][0])