audio-normalizer

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Use when asked to normalize audio volume, match loudness, or apply peak/RMS normalization to audio files.

⭐1stars🔀0forks📁View on GitHub🕐Updated Dec 17, 2025
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When & Why to Use This Skill

The Audio Normalizer skill is a professional-grade utility designed to standardize audio volume levels across various formats. It addresses the challenge of inconsistent loudness in media production by offering Peak normalization, RMS normalization, and LUFS matching (EBU R128/ITU BS.1770). This tool ensures that podcasts, music tracks, and video audio meet industry standards, providing a uniform listening experience while preventing digital clipping and preserving audio fidelity.

Use Cases

  • Podcast Production: Automatically normalize multiple voice tracks to a consistent RMS level, ensuring clear and balanced dialogue throughout an episode.
  • Music Playlist Leveling: Apply peak normalization to a collection of songs to ensure uniform maximum volume across a playlist without distortion.
  • Broadcast & Streaming Compliance: Match audio files to specific LUFS targets (e.g., -16 LUFS for streaming or -23 LUFS for broadcast) to meet platform requirements.
  • Batch Media Processing: Efficiently process entire directories of audio files to apply uniform gain adjustments, saving significant time in post-production workflows.
  • Video Sound Preparation: Standardize audio levels from diverse recording sources before importing them into video editing software for final mixing.
nameaudio-normalizer
descriptionUse when asked to normalize audio volume, match loudness, or apply peak/RMS normalization to audio files.

Audio Normalizer

Normalize audio volume levels using peak or RMS normalization to ensure consistent loudness across files.

Purpose

Volume normalization for:

  • Podcast episode consistency
  • Music playlist leveling
  • Speech recording standardization
  • Broadcast loudness compliance

Features

  • Peak Normalization: Normalize to maximum peak level (dBFS)
  • RMS Normalization: Normalize to average loudness level
  • Loudness Matching: Match LUFS target for broadcast compliance
  • Batch Processing: Normalize multiple files to same level
  • Format Preservation: Maintain original audio format
  • Headroom Control: Prevent clipping with configurable headroom

Quick Start

from audio_normalizer import AudioNormalizer

# Peak normalization to -1 dBFS
normalizer = AudioNormalizer()
normalizer.load('input.mp3')
normalizer.normalize_peak(target_dbfs=-1.0)
normalizer.save('normalized.mp3')

# RMS normalization for consistent average loudness
normalizer.normalize_rms(target_dbfs=-20.0)
normalizer.save('normalized_rms.mp3')

# Batch normalize all files to same level
normalizer.batch_normalize(
    input_files=['audio1.mp3', 'audio2.mp3'],
    output_dir='normalized/',
    method='rms',
    target_dbfs=-20.0
)

CLI Usage

# Peak normalization
python audio_normalizer.py input.mp3 --output normalized.mp3 --method peak --target -1.0

# RMS normalization
python audio_normalizer.py input.mp3 --output normalized.mp3 --method rms --target -20.0

# Batch normalize directory
python audio_normalizer.py *.mp3 --output-dir normalized/ --method rms --target -20.0

# Show current levels without normalizing
python audio_normalizer.py input.mp3 --analyze-only

API Reference

AudioNormalizer

class AudioNormalizer:
    def load(self, filepath: str) -> 'AudioNormalizer'
    def normalize_peak(self, target_dbfs: float = -1.0, headroom: float = 0.1) -> 'AudioNormalizer'
    def normalize_rms(self, target_dbfs: float = -20.0) -> 'AudioNormalizer'
    def analyze_levels(self) -> Dict[str, float]
    def save(self, output: str, format: str = None, bitrate: str = '192k') -> str
    def batch_normalize(self, input_files: List[str], output_dir: str,
                       method: str = 'rms', target_dbfs: float = -20.0) -> List[str]

Normalization Methods

Peak Normalization

  • Scales audio so highest peak reaches target level
  • Preserves dynamic range
  • Good for preventing clipping
  • Target: typically -1.0 to -3.0 dBFS

RMS Normalization

  • Scales audio so average level reaches target
  • Better for perceived loudness matching
  • Good for podcasts and speech
  • Target: typically -20.0 to -23.0 dBFS

LUFS Matching

  • Integrated Loudness Units relative to Full Scale
  • Broadcast standard (EBU R128, ITU BS.1770)
  • Target: -23 LUFS (broadcast), -16 LUFS (streaming)

Best Practices

For Podcasts:

normalizer.normalize_rms(target_dbfs=-19.0)  # Speech clarity

For Music:

normalizer.normalize_peak(target_dbfs=-1.0)  # Preserve dynamics

For Broadcast:

normalizer.normalize_rms(target_dbfs=-23.0)  # EBU R128 compliance

Use Cases

  • Podcast Production: Consistent volume across episodes
  • Music Playlists: Even loudness for continuous playback
  • Audiobooks: Standardized narration levels
  • Conference Recordings: Normalize different speakers
  • Video Production: Match audio levels before mixing

Limitations

  • Does not apply dynamic compression (use separate compressor)
  • Does not remove DC offset (pre-processing recommended)
  • Peak normalization won't match perceived loudness
  • Doesn't fix clipped audio (distortion is permanent)