audioenhancer 0.0.2

A production-grade Python audio enhancement toolkit

AudioEnhancer

A production-grade Python audio enhancement toolkit.

Why AudioEnhancer?

AudioEnhancer is designed for developers who need robust, high-quality audio post-processing without complex configurations. By decoupling algorithms into standardized strategies, it offers a clean, extensible, and pattern-driven API for noise removal, level normalization, and seamless format conversions.

Features

• 🤫 Noise Removal: Spectral gating for stationary noise and median filtering for impulse clicks/pops.
• 🎚️ Loudness Normalization: Support for Peak, RMS, and broadcast-standard LUFS (ITU-R BS.1770) normalization.
• 🔁 Format Conversion: Convert easily between standard formats like WAV, MP3, FLAC, and more.
• 🛠️ Fluent Pipeline Builder: Build and execute custom chains using a modern builder API.
• 🖥️ Command-Line Interface (CLI): Powerful, Click-powered command-line interface for quick batch or single file processing.

Prerequisites

This project requires FFmpeg to be installed and available in your system's PATH.

• macOS: brew install ffmpeg
• Ubuntu/Debian: sudo apt-get update && sudo apt-get install -y ffmpeg
• Windows: Install via Chocolatey choco install ffmpeg or download binaries directly.

Installation

Standard Installation (from PyPI)

pip install audioenhancer

Installation from Source

1. Clone the repository:

   git clone https://github.com/CodeWithBotinaOficial/audio_enhancer.git
   cd audio_enhancer
   

2. Create a virtual environment and install dependencies:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   pip install -r requirements.txt
   

Usage

Command-Line Interface (CLI)

The CLI provides a quick way to process audio files. You can run it as a module or after installing the package:

python -m audio_enhancer process-file examples/sample_audio/input.wav examples/output/output.mp3 --noise-profile spectral_gate --normalize lufs --lufs -16.0

Library Usage

The project can be used as a Python library by importing the AudioEnhancer facade:

from audio_enhancer import AudioEnhancer
from audio_enhancer.normalization.lufs import LufsNormalizer
from audio_enhancer.noise_reduction.spectral_gating import SpectralGatingNoiseReducer

def main():
    # Create an enhancer instance
    enhancer = AudioEnhancer.get_instance()

    # Load an audio file
    audio = enhancer.load_audio("examples/sample_audio/input.wav")

    # Create a processing pipeline
    pipeline = (
        enhancer.get_builder()
        .add_step(SpectralGatingNoiseReducer())
        .add_step(LufsNormalizer(-16.0))
        .build()
    )

    # Apply the pipeline
    enhanced_audio = pipeline.process(audio)

    # Export the result
    enhancer.export_audio(enhanced_audio, "examples/output/enhanced_audio.flac", "flac")
    print("Audio enhancement complete.")

if __name__ == "__main__":
    main()

Design Patterns

This project serves as a practical example of various software design patterns:

• Facade: AudioEnhancer class provides a simple, high-level interface.
• Strategy: Different normalization and noise reduction algorithms are implemented as strategies.
• Builder: PipelineBuilder constructs complex processing pipelines step-by-step.
• Singleton: The Logger and AudioEnhancer use the Singleton pattern.
• Factory Method: Used to create different audio processors.

Documentation

Full documentation, including tutorials, guide, and API reference, is available at https://codewithbotinaoficial.github.io/audio_enhancer/.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Copyright (c) 2026 Diego Alejandro Botina (CodeWithBotina).