create deepfilternet_wrapper

src/refinement/__init__.py

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+# __init__.py
+
+import logging
+from datetime import datetime
+
+# Configure logging
+logging.basicConfig(
+    format='%(asctime)s : %(message)s',
+    level = logging.INFO
+)
+
+logging.info("freq-split-enhance/refinement package has been imported.")

src/refinement/deepfilternet_wrapper.py

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+import os
+import torch
+from df.enhance import enhance, init_df, load_audio, save_audio
+
+def noisereduce(input_audio_path, output_audio_path, model_path=None):
+    """
+    Apply noise reduction using DeepFilterNet.
+    
+    Args:
+        input_audio_path (str): Path to the input noisy audio file.
+        output_audio_path (str): Path to save the enhanced audio file.
+        model_path (str, optional): Path to a custom DeepFilterNet model. Defaults to None (uses the pre-trained model).
+
+    Returns:
+        str: Path to the enhanced audio file.
+    """
+    if not os.path.exists(input_audio_path):
+        raise FileNotFoundError(f"Input file {input_audio_path} not found")
+    
+    # Initialize DeepFilterNet model
+    model, df_state, _ = init_df(model_path)
+    
+    # Load audio
+    audio, _ =  load_audio(input_audio_path, sr=df_state.sr())
+    
+    # Ensure output path exists
+    os.makedirs(os.path.dirname(output_audio_path), exist_ok=True)
+    
+    # Apply noise reduction
+    enhanced_audio = enhance(model, df_state, audio)
+    
+    # Save the enhanced audio
+    save_audio(output_audio_path, enhanced_audio, df_state.sr())
+    
+    return output_audio_path